| Journal of Information and Communications Technology:
Algorithms, Systems and Applications
Received: 03 May 2025; Revised: 27 May 2025; Accepted: 07 June 2025; Published Online: 10 June 2025.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2026, 1(1), 25304 | Volume 1 Issue 1 (June 2025) | DOI: https://doi.org/10.64189/ict.25304
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Security System Using Face Recognition: Machine
Learning Based Approach
Satish Asane, Sushilkumar S. Salve,
*
Athrav Potdar, Abhishek Wagh and Mukesh Nilwarn
Department of Electronics and Telecommunications Engineering, Sinhgad Institute of Technology, Lonavala, Maharashtra, 410401,
India
*Email: sushil.472@gmail.com (Sushilkumar S. Salve)
Abstract
The rising occurrences of illegal entry and security breaches have made guaranteeing safety inside residential
societies a key worry in the contemporary day. Often lacking in consistent and tamper-proof access control are
traditional security solutions like manual guarding, RFID cards, or keypad locks. These traditional approaches
are vulnerable to human error, duplication, and illegal use, hence stressing the critical need for a more smart
and automated solution. Recent studies in the area of machine learning and computer vision have produced
encouraging findings in facial recognition technologies. Many current methods, meanwhile, are costly, lack real-
time processing, or need high-end computing equipment. Research suggests building a society security system
using face recognition technique and machine learning to solve these issues, with the goal of producing a low-
cost, efficient, and frictionless access control system. Reducing dependence on manual monitoring, research
intends to develop a scalable and efficient solution for community- level security systems that offers a more safe
and automated access control system. Using a facial recognition model coupled with Support Vector Machine
(SVM) classification, the proposed security system accurately identifies authorised faces. Testing on a dataset
of authorised and unauthorized individuals revealed an overall accuracy of 95%. The model showed good real-
time performance, fast response time, and resilience to changing lighting conditions and facial emotions.
Attempts at unauthorized access were efficiently spotted and denied, hence guaranteeing improved security.
The time delay between the Email notification is 3-4 sec and false acceptance rate is 3-5% it depends on
threshold. The face detection time is 3-4 sec but it mainly gets affected by the network conditions. Servo
operation time is 5 sec.
Keywords: Support Vector Machine; Raspberrypi4; Face Detection; Image Processing; Distinctive Characteristic
Location; Camera; Servo motor.
1. Introduction
Over the last several years, conventional technology and biometric technology have offered society security
requirements a surprising number of options.
[1]
Some traditional security systems, for example using keys,
passcodes, ID cards, and/or RFID cards, can be unreliable if items for access are stolen. Such security systems
have drawbacks when access is stolen by those without the permission to obtain access and also daily activities
occasionally compel someone to leave the house empty, such as during work or school hours. This weakens
society's security and renders the residence open to break into and theft. Often, these traditional systems are
susceptible to human mistakes, manipulation, and inefficiencies.
[2]
Given these difficulties, our initiative offers a
creative, AI- powered approach: a Society Security System employing Machine Learning and Face Recognition
Technique. Deploying modern technology that identifies faces in real- time and permits or refuses access
accordingly, the aim is to improve the general safety, convenience, and intelligence of community entry
systems.
[3]
Facial recognition in computer vision is the process of comparing a person's facial information
pattern with database picture data to get a face image match. Stored photographs in the database are those from
a training process, specifically from entering as many images of a person's face as practical to boost accuracy.
[4]
Among the several image classification and feature extraction methods used in face recognition are
Convolutional Neural Networks (CNNs), Principal Component Analysis (PCA), Eigenfaces, Local Binary Patterns
(LBP), and Support Vector Machines (SVMs). CNN-based approaches have demonstrated high robustness and
recognition accuracy under varying facial expressions, poses, and illumination conditions.
[3,5]
Cosine similarity
is an excellent measure for determining the similarity between two vectors in terms of the cosine of the angle
between them. For facial identification, a cosine value near zero indicates that the characteristics of unmasked
and masked faces are closely similar and so probably belong to the same individual. With features being vectors
retrieved using Machine learning methods such Convolutional Neural Networks (CNN) and (SVM),
[2,6]
this
approach enables exact matching of facial photos. The Raspberry Pi, a small and reasonably priced computer
device powering the smart security module's features, is at the core of the system.
[7]
IoT-enabled facial
recognition systems based on Raspberry Pi have demonstrated that low-cost embedded platforms can provide
efficient and reliable door access control for smart home and security applications.
[8]
Connected with simple
wiring and armed with a high-resolution camera, the device records video streams of anyone entering the
community entrance. Python, a flexible programming language famous for its vast ecosystem of machine
learning and computer vision tools, is then used to process video; SVM procedure starts with its assistance.
Recently, support vector machines (SVMs) have been suggested as a quite successful tool for general purpose
pattern recognition. Given a set of points belonging to two classes, an SVM intuitively identifies the hyperplane
that separates the biggest possible proportion of points of the same class on the same side, while maximising the
distance from either class to the hyperplane. Hyperplane minimises the danger of misclassifying not just the
examples in the training set but also the unseen examples of the test set.
[9]
The system combines several facial
recognition technologies each of which plays a vital role to precisely identify and recognize faces by means of
picture acquisition and processing, OpenCV offers basic support for face detection utilising conventional
techniques such as Haar cascades and current DNN-based models. Forming the basis for face comparisons, Dlib
finds important facial landmarks and 128-dimensional facial embeddings. Built on Dlib, Face Recognition is a
wrapper that enables identification of people with only a few lines of code. Especially beneficial in different
lighting situations or non- frontal perspectives, MTCNN Multi-task Cascaded Convolutional Neural Networks
provides high- accuracy face detection. A strong system called DeepFace guarantees great dependability over
different facial characteristics and datasets across several recognition backends.
[10]
Robust machine learning and deep learning frameworks like TensorFlow, Keras, and PyTorch train and fine-
tune classification models, hence effortlessly supporting these face recognition components. In some hybrid
setups, lightweight models such XGBoost and LightGBM are employed to accelerate recognition while
preserving acceptable accuracy. Anti-spoofing technologies using infrared cameras, OpenCV- based liveness
detection, and face anti-spoofing models help to prevent illegal access via printed images or video replays, hence
strengthening security even more. Facial data is protected using AES-based data security policies and OpenSSL
encryption. All access attempts whether successful or denied are logged into a secure backend database, which
may be implemented using SQLite or MySQL for local storage, or Firebase and MongoDB for mobile and cloud-
based access. These records contain timestamps, user IDs, and facial photographs for administrative audit or
inspection.
[11]
Based on several previous investigations, research developed a room security system using deep machine
learning technology built with facial recognition verification. Usually, room security in companies or buildings
is still merely manual locks on doors, fingerprints, passwords, and Radio Frequency Identification (RFID) cards
to open doors. Using manual keys not only complicates system construction using room door security products
already on the market but also makes it easy to copy, leave behind, or lose keys. One for a room security system
is facial recognition. Access to the room can be limited such that it is done by specific people whose faces have
been recorded in advance using the training process so that if they open the door, that person must scan his face
then the system will recognize the face. The door will open if the face can be confirmed. Otherwise, the
mechanism will refuse entry to open the door.
[3]
The system offers user-friendly dashboards and interfaces built
using Tkinter and PyQt for desktop applications, and HTML/CSS/JS or Streamlit Dash for web- based platforms
for users and administrators. These APIs provide fast access pattern and anomaly analysis, resident data
management, and real-time monitoring. The four contemporary fundamental processes in constructing any
biometric recognition system are face detection, preprocessing, feature extraction, and face recognition. It
facilitates people's identification and authentication. A video camera or database import initially obtains the
image. It then goes through more processing at several points. This stage's main job is thought to be to find the
target face image from a taken image or one chosen from a database. Actually, the face detection process's main
objective is to decide if a given image has a face image area or not. The output will be delivered to a preprocessing
stage so that further advances can be made when the target facial region or region of concern has been
completely detected and segmented. Usually, the three key components of the picture preprocessing process
are token matching, edge detection, and histogram equalisation. These modules improve image quality and find
the edge point in the digital image; then, using pre-defined algorithms, they carry out the removal and
normalisation. Pre-processing methods erase all undesirable image effects like noise, distortion, blur, shadow,
or filters. This normalises the image to generate a smooth face image as output, which is subsequently applied in
the extraction step.
[12]
Recent advances in face recognition technology have made automated solutions significantly more effective, safe,
and relevant.
[13]
Many methods and implementations have been proposed in the literature, each with certain
benefits and drawbacks. This part gathers notable techniques from recent studies. In 2024, Nasreen Dakhil
Hasan and Adnan M. Abdulazeez offered a deep learning-based facial identification approach. By boosting the
system's automation and security, approach makes it more valuable in practical environments. Still problematic,
however, are privacy and ethical concerns especially as deep learning systems manage more and more private
biometric data.
[14]
Sunardi et al. enhanced facial recognition systems using machine learning in 2023. Their
method hastened several authentication processes and increased accuracy. They did, however, struggle with
data storage and occasional accuracy issues, highlighting the need of balanced infrastructure and data
management.
[3]
Aasawari Boxey and colleagues applied face recognition in a practical purpose using a Raspberry
Pi. Their work was focused on creating a reasonable and simple embedded systems solution. Though cheap and
easy to use, the method struggled with large data volumes and real-time processing, which are common in
practical applications.
[15]
ElBedwehy et al. suggested an approach based on Relative Gradient Magnitude
Strength. Two key features of face recognition, their approach showed great accuracy and robustness to lighting
changes. The idea has notable drawbacks, though, such the potential for baseless accusations and a lack of
transparency in its decision-making process.
[10]
Nourman S. Irjanto and Nico Susantha finally deployed
Convolutional Neural Networks (CNNs) for facial recognition. Approach offers strong performance and the
capacity to quickly learn complex facial features. Notwithstanding its successes, the approach employed a big
labelled dataset and made it difficult to grasp how the model functioned inside.
[2]
2. Methodology
Research mainly focuses on to use facial recognition technology to create an intelligent, contactless, and secure
admission system for residential societies. Using a camera, the system is intended to automatically identify and
detect the faces of both residents and guests. A pre-trained database is then used to decide whether to allow
admission. A well- coordinated pipeline of machine learning, software, and hardware is used to accomplish this.
Here is a detailed account of how we constructed it. We started by putting together the hardware required to
take and process facial photos. Our setup's brain is a Raspberry Pi, which is linked to a camera module to record
live video at the society entrance. Stability and seamless connections are guaranteed via power supply units and
connection cables. The Raspberry Pi can activate door mechanisms via GPIO General Purpose Input Output pins
or a Arduino module if access control such as automatic door unlocking needs to be included.
[16]
Based on the
outcomes of face recognition, this enables us to physically restrict access. Real-time image processing and face
detection are aided by OpenCV. We can find faces in live video streams by using its deep learning-based DNN
detectors or Haar cascades. Unique facial features, known as 128-dimensional facial embeddings, are then
extracted using Dlib.
[10]
These embeddings function similarly to a person's digital fingerprint. With just a few
lines of Python code, we also used Face Recognition by Adam Geitgey, a wrapper over Dlib that greatly simplifies
procedure. we employed MTCNN multi-task cascaded convolutional networks, which is renowned for its deep
learning-based face identification, to increase detection accuracy, particularly in low light or tilted faces. Since
DeepFace supports several backend models, including ArcFace, VGG-Face, and Facenet, it was introduced for
situations where we desire more control and flexibility.
[17]
Every recognized face is processed, identified, and
highly accurately compared to our database thanks to this coupled configuration. We employed machine
learning frameworks such as these to gradually increase the intelligence of our system. For training unique face
recognition or classification models, use TensorFlow and Keras. These enable us to fine-tune models to identify
unfamiliar faces or adjust to variations in facial features due to age or accessory use, for example. PyTorch was
retained as a backup for further investigation or experimentation. In order to classify faces based on the
embeddings, Scikit-learn was used to apply traditional models such as SVM and KNN. Lightweight classification
using XGBoost or LightGBM was investigated for advanced decision-making or hybrid systems. As a result, the
system is not only clever but also responsive, able to learn from fresh data and gradually improve its access
choices. For lightweight, local setups, we utilised MySQL or SQLite. These hold data such as the resident's name,
ID, contact information, and a link to their facial recognition. Real-time monitoring and logs accessible from a
mobile device were made possible by the integration of Firebase as a cloud database. Flexible storage
requirements were met by using MongoDB, particularly when working with unstructured data such as logs,
pictures, or timestamped entries
.[18]
Every time someone enters the building, a timestamp is safely recorded and
may be sent to the cloud for administrator oversight. We may define endpoints for tasks like adding a new user,
confirming a face, reviewing logs, and managing the door with the aid of Flask and FastAPI. The security system
can be readily extended into a fully functional society management tool in the future with the help of modular
API framework. The foundation of every face recognition system is security. We included the following to stop
illegal access to images or videos. Utilising OpenCV and IR cameras, Liveness Detection looks for genuine facial
movements like head tilt or blinking. Face Anti-spoofing Models are systems that are taught to recognise
spoofing attempts, such as when someone holds up a picture. Data is encrypted using OpenSSL and AES,
guaranteeing that face information is never sent or stored in plain text.
Fig. 1 represents the stimulated block diagram, the first block of process is acquisition. In this system, the image
acquisition procedure starts with cv2.VideoCapture(0) initialising the webcam, hence enabling the Raspberry
Pi to constantly access live video frames. Two important functions- show camera and capture and recognize
processes these frames in real time. The show camera function renders the live video feed into a Tkinter GUI
window. Converting each frame into a compatible format PIL. ImageTk and refreshing the display at roughly 30
ms intervals does this. At the same time, capture and recognize gathers individual frames at timed intervals
every five seconds for facial recognition analysis. Without stressing the CPU with frame-by-frame calculations,
this is vital to strike performance and system responsiveness. Featuring a video display window, a status label
for messaging, and a button for manually activating the door servo, the GUI is made up using Python's Tkinter
toolkit. By guaranteeing that high-quality, real-time picture data is consistently recorded and made available for
subsequent processing and recognition, which is fundamental to any intelligent surveillance or access control
system, the collection phase basically underpins the authentication process. In machine learning and computer
vision, the way images are represented mathematically greatly influences a system's ability to "see" and
interpret them. Especially for object detection, the Histogram of Orientated Gradients (HOG) is among the most
efficient image preprocessing methods. HOG does not directly identify objects. Rather, it transforms raw picture
pixels into a systematic description of edge orientations. Machine learning algorithms like SVMs or neural
networks find it easier to learn differentiating characteristics of objects e.g., humans, cars, or animals in a
mathematically relevant way as a result of change.
Fig. 1: Stimulated block diagram.
The second stage of the process is mainly called as preprocessing. Once a frame is taken from video feed, it has
to be pre-processed to guarantee compatibility with the identification engine. As the face recognition library
needs RGB input for precise face detection preparation in system begins with the transforming image from
OPEN CV native BGR format to RGB using cv2.cvtColor(). The detection o human faces in the frame, done by face
recognition. Face encoding, is the main preprocessing activity. Depending on the system settings this method
internally calls a face detection model based on either HOG or a CNN architecture. Detecting facial landmarks
eyes, nose, mouth-the library aligns and cuts a face to a conventional size by means of orientation adjustment.
It guarantees that before more investigation head posture and tilt are normalized. Reducing the variability of
input data produce by various lighting condition, backdrops and camera angles require appropriate
preprocessing. Should no face be identified during this phase, the system reports ‘no person found’, so
guaranteeing that identification only happens when a clear and aligned facial image is present. The next feature
is extraction.
(1)
(2)
and
are the horizontal and vertical gradients, respectively.
Now, by using the gradient components, we can calculate the magnitude and orientation of the particular
gradient at each and every pixel level
(3)
(4)
Here in equation (3) the total strength of the gradient is calculated, equation is derived from Euclidean norm of
the gradient vector. Equation (4) describes the direction of the gradient which is measured in radians or degrees.
These both equations are important for the edge detection.
(5)
Equation gives us the information about gradient orient information and describe it as a long 1D array.
The third step is featuring extraction it is very important step in this approach feature is managed by the face
recongnition. Face encodings functions, which turns a cropped and aligned face into a 128-dimensional feature
vector. Usually depending on the ResNet architecture, a deep convolutional neural network trained on a large-
scale data set of human faces produces these vectors. Robust to small variations in facial expression,
illumination, and head position, the CNN compresses important facial features such as distances between facial
landmarks, textures, and spatial relationship of facial areas into a compact embedding. Perfect for biometric
identification, these embeddings are unique for each person but constant across several photos of the same
subject. Known embeddings are loaded by the system from a serialized file holding a list of face vectors and
matching IDs or names. Students and EncodeListKnown keep list in memory. The strength of method is in
abstracting high-dimensional pixel data into a fixed-length numerical representation that can be quickly
compared using basic mathematical procedures. The quality of the embeddings directly influences the accuracy
of recognition and decision-making; this stage closes the gap between machine learning classification and raw
picture input. CNNs have transformed the way machines view the world. Their capacity for automatic feature
extraction from raw image data-a process once demanding manual engineering using methods as HOG, SIFT, or
SURF-is among their most potent gifts. Unlike conventional algorithms, CNNs learn directly from the data
hierarchical patterns including edges, textures, and object components. Modern computer vision applications
include picture categorisation, object detection, and facial recognition are built on these learnt patterns- or
features.
(6)
Equation describes mainly the idea of 2D convolution operation, a major concept in image processing and CNN.
Equation calculates the convolution of an image.
Activation function:
(7)
Fully connected Layer:
(8)
In equation w represents weight matrix, x is input vector and b is bias.
The fourth step is face database Our facial recognition-based security solution uses the face database as memory;
all the registered faces are kept there in a format the computer can grasp. The method keeps feature vectors-
mathematical representations of each person's distinct facial features-rather than storing photos as a
conventional album. Every system-registered individual has a particular collection of facial characteristics
recorded into a 128- dimensional vector. You may consider this as assigning each face a unique fingerprint;
rather than using ink, we use numbers. Stored in the face database, these vectors serve as the reference for
every recognition test. The algorithm checks for a match by comparing their newly acquired face data to these
kept vectors whenever someone shows at the door. Every database record is connected to a particular identity-
like a name, ID number, or user tag. Thus, when a match is discovered, the system does not just declare "face
matched"; rather, it identifies who it matched.
The Fifth step is training in this step the system pulls from each of these annotated photographs what is called
a feature vector a set of numerical values that reflect the unique qualities of that person's face. The training set's
basis is formed by these vectors, which are then used to instruct machine learning models on how to distinguish
between individuals. A classifier, which is a kind of algorithm that learns how to group or separate several
identities, takes feature vectors once they are retrieved and feeds them in. Among the most often used classifiers
in face recognition are k-Nearest Neighbours (KNN) picks the most comparable match by comparing fresh
faces to the nearest known ones. Support Vector Machines (SVM) draw exact boundaries in the data to
distinguish one face from another. A strong ensemble method for structured data, XGBoost provides great
performance. In increasingly sophisticated systems, deep learning models are employed; these models can learn
even more delicate aspects by passing the face data through several neural network layers. Often, these finish
with an SVM-based or softmax layer determining the last ownership decision on the face.
The Sixth Step is a classifier it important step it is Based on the retrieved face traits, the classification stage
defines a person's identification. The present implementation of the system uses face_recognition. Compare
faces and face_distance to compare the unknown face's encoding with all known encodings, hence performing
a nearest-neighbor categorisation. While the latter calculates the Euclidean distances between vectors, the
former evaluates whether any known face is near the unknown under a specified threshold (0.5). The best
match is the encoding with the least distance. The related name is given back if the match is legitimate and under
the distance limit; otherwise, the face is noted as unknown person and an email warning is sent. Although
efficient for tiny datasets, method is inaccurate and ineffective with more faces. A better option would be
combining scikit-learn's Support Vector Machine (SVM) classifier into use. Trained on the embeddings with
known identities, an SVM produces decision boundaries in high- dimensional space. Using these acquired
bounds, the SVM may rapidly classify new embeddings at prediction time, hence providing more scalability and
accuracy. Replacing distance- matching with an SVM model could help the system to be more accurate and
responsive, particularly in bigger, more varied setting.
We have used face recognition compare faces and face distance method in our system the SVM plays an
important role in this whole procedure. A supervised machine learning technique called support vector machine
(SVM) classifies data by locating an ideal line or hyperplane maximising the distance between every class in an
N-dimensional space. In linear classification, the equation wx + b = 0 defines a hyperplane, or decision border
dividing many classes in feature space. The nearest data points to the hyperplane, which are vital for SVM
hyperplane and margin definition, are support vectors. The distance separating the hyperplane from the
support vectors defines the margin. SVM tries to maximise this margin for improved classification performance.
A function called kernel translates data to a higher- dimensional space, hence allowing SVM to manage non-
linearly separable data. Soft Margin: When data is not totally separable, it inelets some misclassifications by
adding slack variables, hence balancing margin maximising and misclassification penalties Soft Margin: When
data is not totally separable, it lets some misclassifications by adding slack variables, hence balancing margin
maximising and misclassification penalties.
Hard Margin: A maximum-margin hyperplane that perfectly separates the data without misclassifications.
Soft Margin: When data is not totally separable, it lets some misclassifications by adding slack variables, hence
balancing margin maximising and misclassification penalties.
(9)
Equation gives us the idea about decision boundary it is also called as hyperplane that separates data into two
classes where W is t h e normal vector to the hyperplane and b is the offset or bias representing the distance of
the hyperplane from start
(10)
Where w is always positive and represent Euclidean norm of weight vector w. This support vector is near to close
to the boundary. The purpose of equation is to give idea about the distance between hyperplane and support
vector.
System initialization sets the beginning point. All the hardware components including the camera, computer
vision modules, door locking system, and database servers are powered up and synchronized. The system
verifies whether all sensors and modules are functioning during this phase and is ready to notice a person's
presence. Arrangement guarantees immediate system response should someone approach the door. Once the
system is running, it continuously examines its surroundings via a camera. The system detects movement or
finds a human face utilizing detection techniques such as Haar cascades, HOG (Histogram of Orientated
Gradients), or deep learning-based face detectors when someone arrives and stands in front of the camera. This
detection starts the following phase in which the face image must be recorded. The third process is the block is
capturing face images in this state the main process of image capturing happens. It is very important for the
system that the image capturing happens.
Fig. 2 Imagine a situation where you’re looking at a bunch of red and blue circles distributed on a plane. SVM a
powerful tool used for classification. We see two different groups red circles and blue circles. Each of these
points exists in a two-dimensional space defined by two features, labelled as X1 and X2. To sum it up, a SVM
works by finding the cleanest, widest dividing line between two groups of data. Email spam detection to medical
diagnosis, where clear and confident decisions are essential. Fig. 3 is the flow chart of whole process. Once the
system identifies a person, it records one or more photographs of their face. The system might preprocess the
image by cropping it to concentrate on the face, changing lighting or contrast, and properly aligning the face-for
example, making sure the eyes are horizontal-to guarantee quality recognition. A clean and well-aligned image
greatly improves the accuracy of the recognition system; hence these preprocessing actions are absolutely vital.
Feature extraction happens in this phase on the collected and pre-processed face image; a deep learning model
transforms the face into a distinct vector (facial embedding). The database's stored embeddings are then
compared to this embedding. Similarity measures like Euclidean distance or cosine similarity help to determine
how near the captured face is to current records. A small enough distance below a specified threshold-indicates
the face might already exist in the system.
Fig. 2: Classification using SVM.
Fig. 3: Flow chart.
It show a system decides at phase of decision-making if the recorded face corresponds to any database record.
Should a matching face be discovered that is, the similarity score satisfies the threshold the system deems the
match successful and moves on. Access is denied if no matching record exists; the system may optionally report
the unsuccessful attempt or notify security staff. Maintaining the security and integrity of the access control
system depends on this verification process. A match is successfully confirmed when the identity of the
individual standing in front of the door matches a registered user in the database. The system might run further
checks like user permissions, time-based access privileges, or event logging for security auditing before it
unlocks the door. The system only prepares the unlock instruction after all required validations are passed. At
last, the system unlocks the door by sending an electrical signal to its locking mechanism. This could mean
activating a motorized bolt, unlocking a magnetic lock, or tripping a relay. The door stays open for a specified
time to let the confirmed individual inside. The system then re-locks the door and resets itself, ready for the
next authentication cycle. Based on facial recognition, this last stage guarantees smooth yet safe access
management.
Fig. 4 provides us a virtual representation of hardware components used in this research at the heart of the
system is the Raspberry Pi4 Model B which is connected to a camera model to identify faces. We also have servo
motor and memory card which are used for to make motion and to save data respectively. Indicated with notable
improvements over its forerunners, the Raspberry Pi 4 is the most powerful device in the Raspberry Pi series.
Its Quad-core Cortex-A72 CPU running at 1.5GHz offers a tremendous performance increase appropriate for
machine learning and complicated picture processing. Available in 2GB, 4GB, and 8GB RAM versions, the Pi 4
supports multitasking and can run more memory-intensive Python libraries like TensorFlow Lite, OpenCV, Deep
Face, and PyTorch Lite.
Fig. 4: Schematic representation of the proposed prototype.
Useful for configurations needing GUI monitoring, the Raspberry Pi 4 also supports dual monitor output using
two micro-HDMI connections. It permits quicker data transfer to external cameras or storage devices using USB
3.0 connectors. Its Gigabit Ethernet also guarantees consistent and fast internet connection, which is beneficial
for cloud- based monitoring systems. Stable performance under load is guaranteed by a 5V/3A USB-C power
supply powering the board. Even with several individuals in the database, the Pi 4 can readily manage real-time
video processing, facial feature extraction, and classification utilizing pretrained deep learning models in face
recognition initiatives. Dependable and Efficient Raspberry Pi 3B+. Though older, the Raspberry Pi 3B+ is still
commonly utilized in medium-scale and academic applications because of its dependability and reduced power
utilization. Paired with 1GB LPDDR2 RAM, its Quad.core Cortex- A53 processor runs at 1.4GHz, which is
adequate for light multi- user face recognition systems or single-user use. Among its offerings are an HDMI
output, a CSI camera interface, and four USB 2.0 ports. Though its Ethernet speed is capped at 300 Mbps, it offers
consistent connection for small-scale networking. Powered by a 5V/2.5A micro- USB, the 3B+ is marginally less
power-hungry than the Pi Its compatibility with the whole Raspberry Pi software ecosystem lets users quickly
deploy OpenCV, Dlib, or bespoke Python-based recognition scripts.
The Raspberry Pi 3B+ provides a reasonable substitute for the Pi 4 for systems with less processing needs,
financial limits yet preserving sufficient computing automation. Suitability of Comparison and Use Case
Although both Raspberry Pi 4 and 3B+ can run the basic features of a facia massive face databases, and high-
resolution cameras. Conversely, single-user systems, modest at the installations, and educational presentations
are best served by the Raspberry Pi 3B+. Their centrality to the success of edge-AI research comes from their
capacity to interact with several modules like camera units, relay modules, sensors, and cloud platforms. Both
designs are compatible with significant Python libraries and support Raspberry Pi OS (Linux), hence offering a
smooth development experience. The second component of the system is camera module.
Often used for research involving image gathering, including face recognition systems, the Zebronics Zeb-Crystal
Pro is a USB webcam that provides a reasonable and affordable alternative. Its plug-and-play capability and
compatibility with several operating systems, including Windows, Linux, and Raspberry Pi OS, make this
webcam especially fit for integration with Raspberry Pi-based configurations. With a frame rate of 30 frames
per second, the Zeb-Crystal Pro offers clear and detailed image capture at 720p HD resolution. This degree of
resolution is adequate to precisely capture facial features, which is absolutely necessary for face detection and
recognition tools. The camera guarantees improved clarity and sharpness in obtained photos using a five-layer
precision (5P) lens. Particularly in static environments when the subject stays at a constant distance from the
camera, the manual focus function lets users change the focus by turning the lens ring. Among the most notable
qualities of the Zeb-Crystal Pro is its integrated microphone. This makes the device a flexible recognition
system, the size of the research will determine component for programs that could also need voice
instructions or audio recording since it allows it to record audio with video. Offering mounting and
positioning versatility, the webcam connects using a USB 2.0 interface and includes a long 1.5-meter wire. Its
support of clip-on and flat surface mounting makes it suitable for various hardware setups.
The main image capture tool for a face recognition study is the Zeb-Crystal Pro. The Raspberry Pi then processes
and analyses still photos or live video feed captured by it. The camera's interoperability with OpenCV and other
computer vision frameworks enables smooth integration into Python- based applications. Accessing the camera
stream is as simple as running a script using cv2.VideoCapture(0), which makes it straightforward to include
into more extensive software systems. The servo motor pursue the purpose of door opening or closing. Designed
to enable exact control of angular or linear position, a servo motor is a rotary or linear actuator. Its components
include a DC or AC motor, a feedback mechanism-usually a potentiometer-a gearing system for torque
conversion, and control circuit that interprets input signals and guides motor activity accordingly. Pulse Width
Modulation (PWM), a method whereby the width of the signal pulse defines the location to which the servo
should travel, usually provides the control. Unlike conventional motors, which spin constantly when energised,
servo motors are meant to move to and maintain a certain position. Though continuous rotation and full 360°
versions are also available for certain applications, standard servo motors usually spin between 0° and 180°.
2.1 Operation of a servo motor
Feedback control is the fundamental idea of a servo motor. The motor turns at a particular angle defined by the
pulse's width when a control signal-usually a PWM pulse-is given to the servo. The internal feedback system
then tracks the shaft's present location and compares it to the desired position. The control circuitry modifies
the motor's movement to meet the target location should there be a discrepancy. For example, a 1.5 ms PWM
pulse usually places the servo in its neutral position (90°), but 1 ms and 2 ms pulses send it to 0° and 180°,
respectively. High accuracy, stability, and rapid response time are guaranteed by this constant loop of position
verification and correction.
Usually, a servo motor consists of:
DC Motor: Rotates to enable motion.
Gearbox: Increases torque by lowering the high-speed output of the motor.
Usually, a potentiometer tracks the shaft position of the motor.
Control Circuitry: Adjusts the motor correspondingly by means of the control signal processed and received.
In systems where movement has to be both accurate and repeatable, this combination lets servo motors operate
as exact actuators.
SD cards are the main component used for the storage purpose. The performance, stability, and lifespan of a
Raspberry Pi- based application-especially those with real-time image processing like facial recognition
systems-are greatly influenced by the choice of storage. Often ignored because of its size, the microSD card is
actually the main boot and storage medium for the Raspberry Pi. The whole system is based on a 16GB microSD
card, which hosts the operating system, codebase, facial recognition algorithms, user data, and real-time logs.
The Raspberry Pi would be inoperable without it.
2.1. 1 Main functions
The 16GB microSD card serves several important tasks in a face recognition system.
The Raspberry Pi starts up straight from the microSD card. Essential for executing any applications or services,
it keeps the whole operating system-usually Raspberry Pi OS (previously Raspbian).
All Python scripts, image processing libraries like OpenCV, and machine learning tools like Dlib, TensorFlow
Lite, and Deep Face are run from this card.
Data Logging and Face Storage: The card also keeps the face database-this comprises logs of identified users or
access events, numerical embeddings, and taken face photos. Therefore, this little card serves as both the
memory and brain storage for the system, managing all from startup activities to real-time recognition data.
2.2 Technical specifications and suitability
Research requiring continual data access, such facial recognition, should use a high-quality Class 10 or UHS-I
(Ultra High Speed) 16GB microSD card. Typically, these cards have written speeds of 10 MB/s and read speeds up
to 100 MB/s, which helps keep the real-time responsiveness of the system.
Other noteworthy features are: Storage Size: 16GB (about 14.4GB useable), File System: Originally FAT32,
changed to ext4 following OS installation. Rated for thousands of read/write cycles, they are consistent for
ongoing logging and image capturing.
Compatible with every Raspberry Pi model including Pi 3B+ and Pi 4.
Though 16GB might appear little by current computer standards, it is well-optimized for embedded systems
lacking significant database storage or strong multimedia processing needs.
Practically, the microSD card should be:-
Formatted for correct OS installation with official Raspberry Pi Imager. Cloned or backed up often since frequent
writes, particularly in logging, could wear the card with time.
Used with a Class 10 rating or above to guarantee seamless performance during activities like image capture,
face comparison, or remote server connectivity.
On a 16GB microSD card in such a research, below is a rough distribution of space:
Allotment is adequate for keeping user records and running over a long period in smaller facial recognition
systems-such as attendance systems or smart door locks.
2.3 Data integrity and security
Rated for thousands of read/write cycles, they are consistent for ongoing logging and image capturing.
Compatible with every Raspberry Pi model including Pi 3B+ and Pi 4. Though 16GB might appear little by
current computer standards, it is well-optimized for embedded systems lacking significant database storage or
strong multimedia processing needs.
2.4 Software component
The main programming language in the system is Python. It has emerged as the most popular choice for machine
learning and computer vision research because to its simple syntax, massive community support, and an
extensive range of pre-built libraries. Python lets programmers quickly prototype concepts, include machine
learning models, and readily connect with hardware components such as the Raspberry Pi's GPIO pins. Its
readability and adaptability make it the perfect language for both the user interface and the backend logic of the
system. Its readability and adaptability make it the perfect language for both the user interface of the system
and the backend logic.
In this research many libraries re used one of them is OpenCV. The basis of the module for computer vision. It
processes the image before sending it to identification modules, performs live video stream acquisition, and
detects faces using Haar cascades or deep neural networks (DNN). For real-time applications, it's quick,
lightweight, and efficient. A strong library for extracting 128-dimensional face embeddings-numerical
representations of faces is Dlib. Dlib is also helpful for matching faces and maintaining consistency throughout
recognition since it supports facial landmark detection.
Ageitgey's Face Recognition Built on top of Dlib, this library offers a high-level, simple-to-use interface for face
recognition. With only a few lines of code, it simplifies difficult chores such comparing faces and maintaining
known/unknown face databases. MTCNN, or Multi-task Cascaded Convolutional Networks, especially in
different lighting and position situations, its deep learning-based detector precisely finds facial areas with
enhanced accuracy. It increases accuracy of detection in difficult settings. A flexible platform supporting many
state-of-the-art models including VGG-Face, Facenet, ArcFace, and Dlib. It is helpful for performance comparison
across several backends and provides end-to-end facial recognition pipelines.
A flexible platform supporting many state-of-the-art models including VGG-Face, Facenet, ArcFace, and Dlib. It is
helpful for performance comparison across several backends and provides end-to-end facial recognition
pipelines. The following machine learning libraries are used for training, deployment, and operating of artificial
intelligence models: -
Facial categorisation deep learning models are trained and fine-tuned using TensorFlow / Keras. They let you
create neural networks that can learn to distinguish between faces and confidently assign IDs.
Preferred for research or bespoke model development, PyTorch provides dynamic computation graphs and is a
substitute for TensorFlow. It is beneficial for testing face recognition techniques and novel architectures.
A conventional ML library used to run clustering techniques and classification algorithms including K-Nearest
Neighbours (KNN) and Support Vector Machines (SVM). In situations when deep learning could be too resource-
intensive, these models are very useful. These are optional libraries for quick decision-making in hybrid
recognition systems. They are great for situations when user classification combines facial characteristics with
other metadata. They are great for situations when user classification combines facial characteristics with other
information.
3. Results and discussion
Evaluation metric overview is shown in Table 1. Across Fig. 5 several parts, the face recognition system's
performance measures are really remarkable. While Open CV managed real-time frame processing, Face
Detection Utilizing Open-CV and MTCNN attained a 92-95% accuracy; MTCNN guaranteed strong detection even
under difficult circumstances including lighting changes and occlusions. With a 98.2% accuracy, Face
Recognition with Deep Face, supported by Tensor Flow, did even better, suggesting good model training on
several datasets for great generalization. A 93% accuracy was produced by an additional machine learning
method utilizing KNN and SVM, providing a simple yet powerful solution for resource-limited settings. Real- time
database updates using Firebase took roughly 21 seconds, which is reasonable for non-critical tasks but
indicates room for improvement. Powered by Flask or Fast API, the API response time was an astonishingly low.
Fig. 5: Performance metrics of security system.
Table 1: Evaluation metric overview.
Metric
Formula
Description
Accuracy
(TP + TN) / (TP + TN + FP + FN)
Measures overall correctness
Recall (Sensitivity)
TP / (TP + FN)
How well the model identifies true positives
Precision
TP / (TP + FP)
How well the model avoids false positives
F1 Score
2 × (Precision × Recall) / (Precision + Recall)
Harmonic mean of precision and recall
With response time of just 0.25 sec, the system guarantees real time interaction such as door unlocking ensuring
both speed and efficiency in practical applications. Speaking of which, the door unlock latency was recorded at
1.8 seconds, indicating effective hardware- software interaction. At last, Anti-Spoofing Detection attained a
strong 92-95% accuracy, so guaranteeing a safe authentication process and stressing the effective integration
of liveness detection technologies to guard against spoofing attempts. The graph offers a clear, visual depiction
of the performance parameters obtained during the testing phase of the social security system using face
recognition and machine learning. Every bar in the graph offers both technical knowledge and practical
consequences by reflecting a vital element of the system's operation.
Leading the way, Recognition Accuracy distinguishes itself with a remarkable average of 93.5%, hence stressing
the system's excellent dependability in spotting authorised people in optimal lighting conditions (Table 2). This
accuracy guarantees that the system stays consistent for daily use in community or residential settings. Closely
linked to accuracy are the False Acceptance Rate (FAR) and False Rejection Rate (FRR), which average at 4.0%
and 3.0% respectively. These measures are vital for grasping the security balance: FRR shows how often
legitimate users are denied access; FAR shows how often unauthorised people could be wrongly granted access.
The low percentages in both categories imply the system efficiently controls the trade-off between security and
convenience. Face Detection Speed and Recognition Speed were measured at roughly 0.45 seconds per frame
and 0.75 seconds per face, respectively, in terms of operational speed. These numbers suggest a near real-time
processing capacity that would enable seamless and continuous user interaction. Delays are negligible;
therefore, the entrance experience is seamless. Roughly 3.5 seconds of email notification delay covers the time
required to collect the image, perform recognition, and send a network warning. Though a little influenced by
internet connection, reaction time is still comfortably inside acceptable range for real-time alarm systems. The
Servo Operation Time, or the time the system keeps the door open following facial recognition, is set at 5
seconds. Though short enough to avoid security holes, this is more than enough time for entrance.
Table 2: Parametric review.
Step
Success Rate / Performance
Recognition Accuracy
92-95% with good lighting
False Acceptance Rate(FAR)
3-5% (depends on threshold 0.5)
False Rejection Rate(FRR)
2-4%
Face Detection Speed
0.3-0.6 sec/frame
Recognition Speed
0.5-1 sec/per face
E-mail Notification
Delay
3-4 sec(depends on network)
Servo Operation Time
5 sec
System Startup Time
2-4 sec
Max Supported Faces
50-100 faces
Accuracy is the most natural way to gauge how well a categorisation model works is accuracy. It indicates how
frequently the algorithm either accurately detects a face (True Positive) or properly rejects an unknown (True
Negative). High accuracy-for example, 95% in this instance-indicates that the system nearly always makes the
correct choice, so causing few errors. But if the data is unbalanced-for example, if there are significantly more
genuine users than imposters or vice versa-accuracy by itself can be deceptive since the algorithm could do well
just by consistently forecasting the majority class. So, although great accuracy indicates good general system
performance, it should be read together with Precision and Recall for a whole knowledge.
TP (True Positives) = 50
TN (True Negatives) = 45
FP (False Positives) = 10
FN (False Negatives) = 5
Precision addresses the need for the calculations with the accuracy. It emphasises the accuracy of good
outcomes-that is, how frequently the system's choices to let someone in were right. High precision like 83.3%
here-shows that very few unauthorised. People were wrongly granted access
security-sensitive applications,
precision is particularly important since one False Positive (accepting an imposter) might lead major violations.
In facial recognition security systems, a high Precision assures users and administrators that the systems.
Precision =
Recall addresses still another crucial issue: "Of all actual users who sought access, how many did the system
effectively identify? "It assesses the capacity of the system to identify all actual positives. A high Recall-again,
98.95%- indicates that very few actual users were wrongly turned down. User experience and accessibility
depend greatly on this, particularly in big societies, organisations, or institutions where rejecting a legitimate
user (False Negative) could cause annoyance, operational delays, and loss of confidence. When missing an actual
positive result is expensive, recall is essential; in security, a real employee or resident must not have access
denial.
Recall =50/55= 0.909 *100 =90.9%
The F1-Score tackles the traditional trade-off between Precision and Recall. While Precision focuses on the
accuracy of positive predictions, and Recall on the capacity to identify all positives, the F1-Score combines them
to produce a balanced assessment. This is significant since in many real-world situations, both erroneous
acceptances and false denials are harmful, such as facial recognition security. A system is excessively rigorous if
Precision is high but Recall is low since many real users are excluded. A system is too loose if Recall is high but
Precision is low; numerous imposters are accepted. The F1-Score thereby guarantees that the system is
inclusive as well as safe. A high F1-Score (98.95% here) indicates that the system nearly perfectly balances
accepting legitimate users with rejecting illegal ones.
F1-Score = 2 × (Precision × Recall) / (Precision + Recall)
= 2 × (0.833 × 0.909) / (0.833 + 0.909)
= 2 × 0.757 / 1.742 ≈ 0.869 → 86.9%
Acceptable for non-commercial, community-based implementations, the system kept a false acceptance rate
(FAR) of < 2% and a false rejection rate (FRR) of < 5%. A 16GB Class. 10 microSD card guaranteed consistent
storage and boot operations to fit the Raspberry Pi OS, the whole software stack, and logging up to 5,000 face
detection events with embedded photos and timestamps. From a software standpoint, lightweight web servers
created using Flask or Fast API let mobile apps and dashboards interface at the API level, hence enabling remote
control and monitoring. Scalability has also been taken into account. By upgrading from the Dlib backend to
DeepFace's ArcFace or Facenet model and storing face embeddings in MongoDB or Firebase for cloud-based
access, the modular architecture lets the system grow from managing 50 users to 500+ users. For especially for
task like classifying emails or not spam. It’s important to evaluate how well the model is performing. Accuracy
can be misleading. Precisions are how trustworthy the positive prediction are. Think of F1 score as a way to
balance the two. It’s the harmonic mean of precision and recall which means it gives more weight to low values.
If either precision or recall is low, the F1 score will be low too. It forces your model to be good at both identifying
all the real cases and being accurate when it does.
4. Conclusion
The evolution of a Society Security System utilising Face Recognition and Machine Learning has shown how AI-
powered solutions might change access control in institutional and residential settings. Research effectively
provides a functional, smart, and reasonably priced security system by combining affordable hardware
components including the Raspberry Pi 4, a 720p camera module (Zebronics Zeb-Crystal Pro), and a 5V relay,
with strong software libraries like OpenCV Dlib, DeepFace, and TensorFlow. Depending on the ambient lighting
and camera angle, the system was meant to identify faces in real time with an average detection delay of 0.8–1.2
seconds per face. The face recognition accuracy on a test database of 50 unique users with 10 photographs per
user attained an ideal range of 94– 97% using 128-dimensional embeddings produced by Dlib and compared
using cosine similarity or SVM classifiers. Acceptable for non-commercial, community-based implementations,
the system kept a false acceptance rate (FAR) of < 2% and a false rejection rate (FRR) of < 5%. A 16GB Class 10
microSD card guaranteed consistent storage and boot operations to fit the Raspberry Pi OS, the whole software
stack, and logging up to 5,000 face detection events with embedded photos and timestamps. From a software
standpoint, lightweight web servers created using Flask or FastAPI let mobile apps and dashboards interface at
the API level, hence enabling remote control and monitoring. Scalability has also been taken into account. By
upgrading from the Dlib backend to DeepFace's ArcFace or Facenet model and storing face embeddings in
MongoDB or Firebase for cloud-based access, the modular architecture lets the system grow from managing 50
users to 500+ users. The GPIO interface makes it even more adaptable for multi- layered security by supporting
integration with *more relays, RFID readers, or biometric sensors. From a cost standpoint, the whole setup
comprising the Raspberry Pi (£55), webcam (£15), relay module (~£3), and other accessories remains well
within £80–£100. For tiny institutions seeking sophisticated security infrastructure without relying on
expensive commercial systems in budget- conscious housing societies this is desirable option. Ultimately, this
research not only meets its objective of delivering a safe and automated entrance system utilising facial
recognition but also demonstrates how open-source tools, simple hardware, and smart design can come together
to produce a very practical and deployable solution. The system may be scaled to enterprise grade use with
small improvements-such as face anti spoofing, voice alarms, and database encryption making it a possible basis
for the next generation smart AI driven access control systems.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
The datasets generated and/or analyzed during the current study that support the findings are available from
the corresponding author upon reasonable request.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
Supporting Information
Not applicable.
References
[1]
F. Faisal, S. A. Hossain, Smart security system using face recognition on raspberry Pi, in Proceedings
2019 13th International Conference on Software, Knowledge, Information Management and
Applications (SKIMA), Island of Ulkulhas, Maldives, 2019, 1–6, doi:
10.1109/SKIMA47702.2019.8982466.
[2]
N. S. Irjanto, N. Surantha, Home security system with face recognition based on convolutional neural
network, International Journal of Advanced Computer Science and Applications, 2020, 11, 408– 412,
doi: 10.14569/IJACSA.2020.0111152.
[3]
S. Sunardi, A. Fadlil, D. Prayogi, Room security system using machine learning with face recognition
verification, Revue d'Intelligence Artificial, 2023, 37, 1187–1196, doi: 10.18280/ria.370510.
[4]
A. J. Russ, M. Sauerland, C. E. Lee, M. Bindemann, Individual differences in eyewitness accuracy across
multiple lineups of faces, Cognitive Research: Principles and Implications, 2018, 3, 1–17, doi:
10.1186/s41235-018-0121-8.
[5]
Y. El Madmoune, I. El Ouariachi, K. Zenkouar, A. Zahi, Robust face recognition using convolutional
neural networks combined with Krawtchouk moments, International Journal of Electrical and
Computer Engineering, 2023, 13, 4052–4067, doi: 10.11591/ijece.v13i4.pp4052-4067.
[6]
D. A. Abdullah, D. R. Hamad, I. Y. Maolood, H. Beitollahi, A. K. Ameen, S. A. Aula, A. A. Abdulla, M. Y.
Shakor, S. S. Muhamad, A novel facial recognition technique with focusing on masked faces, Ain Shams
Engineering Journal, 2025, 16, 103350, doi: 10.1016/j.asej.2025.103350.
[7]
S. Pecolt, A. Błazęjewski, T. Królikowski, I. Maciejewski, K. Gierula, and S. Glowinski, Personal
Identification using embedded raspberry pi-based face recognition systems, Applied Sciences, 2025,
15, 887, doi: 10.3390/app15020887.
[8]
A. R. Syafeeza, M. K. Mohd Fitri Alif, Y. Nursyifaa Athirah, A. S. Jaafar, A. H. Norihan, M. S. Saleha, IoT
based facial recognition door access control home security system using Raspberry pi, International
Journal of Power Electronics and Drive Systems, 2020, 11, 417–424, doi:
10.11591/ijpeds.v11.i1.pp417-424.
[9]
G. Guo, S. Z. Li. K. Chan, Face recognition by support vector machines, Proceedings of IEEE
International Conference on Face and Gesture Recognition, 2001, 196– 201, doi:
10.1109/FG.2000.10019.
[10]
M. N. ElBedwehy, G. M. Behery, R. Elbarougy, Face recognition based on relative gradient magnitude
strength, Arabian Journal for Science and Engineering, 2020, 45, 1–18, doi: 10.1007/s13369-020-
04538-y.
[11]
E. Gamess and S. Hernandez, Performance evaluation of different raspberry pi models for a broad
spectrum of interests, International Journal of Advanced Computer Science and Applications, 2022, 13,
819– 829, 2022, doi: 10.14569/IJACSA.2022.0130288.
[12]
T. S. Gunawan, M. H. H. Gani, F. D. A. Rahman, M. Kartiwi, Development of face recognition on Raspberry
Pi for security enhancement of smart home system, Indonesian Journal of Electrical Engineering and
Informatics, 2017, 5, 317-325, doi: 10.52549/ijeei.v5i4.361.
[13]
M. A. Islam, M. T. Ahmed, M. I. Hossain, M. H. Kabir, S. Roy, Face recognition based physical layer
security system for next-generation wireless communication, World Journal of Advanced Research and
Reviews, 2023, 18, 524–532, doi: 10.30574/wjarr.2023.18.3.1099.
[14]
N. D. Hasan, A. M. Abdulazeez, Face recognition based on deep learning: a comprehensive review,
Indonesian Journal of Computer Science, 2024, 13, 3779-3797, doi: 10.33022/ijcs.v13i3.4037.
[15]
A. Boxey, A. Jadhav, P. Gade, P. Ghanti, A. O. Mulani, Face recognition using Raspberry Pi, Journal of
Image Processing and Intelligent Remote Sensing, 2022, 2, 15–23, doi: 10.55529/jipirs.24.15.23.
[16]
M. H. Khairuddin, S. Shahbudin, M. Kassim, A smart building security system with intelligent face
detection and recognition," IOP Conference Series: Materials Science and Engineering, 2021, 1176,
012030, 2021, doi: 10.1088/1757-899X/1176/1/012030.
[17]
N. Surantha, W. R. Wicaksono, An IoT based house intruder detection and alert system using histogram
of oriented gradients, Journal of Computational Science, 2019, 15, 1108– 1122, doi:
10.3844/jcssp.2019.1108.1122.
[18]
R. Syafeeza, M. K. M. F. Alif, Y. N. Athirah, A. S. Jaafar, A. H. Norihan, M. S. Saleha, IoT based facial
recognition door access control home security system using Raspberry Pi, International Journal of
Power Electronics and Drive System, 2020, 11, 417–424, doi: 10.11591/ijpeds.v11.i1.pp417-424.
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