Efficient face detection and identification in networked video surveillance systems

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The document proposes a real-time face detection and identification system using IP cameras and a Raspberry Pi. It relies on open source APIs like OpenCV for image processing capabilities. A study is conducted on the performance of the proposed system compared to commercial frameworks. Various experiments are performed on the developed system and results are analyzed through software codes. The solution provides simple face detection and identification functionalities for both live and still human faces.

Technology

2020 – 2021
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Efficient Face Detection and Identification In Networked Video Surveillance Systems.
Abstract:
Applications for face detection use algorithms that rely on identifying human faces in broader
photos that may include environments, artifacts, and other sections of a person's physique.
This work proposes a real-time identification system, based on modern image processing
capabilities of open source API like OpenCV and due to the solution requirements, a study
on the performance analysis of such solution compared to available commercial framework
like SPID from NEC is intended. However, here, the study is available with the results of
various experiments on the developed system. A systematic approach is followed to produce
such outputs and have been measured using software codes. By using IP camera and a
Raspberry Pi, the solution developed is simple in nature. This study relies on face detection
and identification functionalities for human faces but not limited to live faces only but mix of
faces from still images as well.

The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.

Deep learning for understanding faces

sieubebu

Deep learning methods have improved machine capabilities for face recognition. Three key modules are typically used: 1) A face detector localizes faces in images using region-based or sliding window approaches with DCNNs. 2) A fiducial point detector localizes facial landmarks using DCNN regressors or 3D models. 3) A DCNN extracts features for face recognition and verification by computing similarity scores between representations. Large annotated datasets have enabled DCNNs to learn robust representations for unconstrained images.

Social distance and face mask detector system exploiting transfer learning

IJECEIAES

As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.

IRJET- Vehicle Seat Vacancy Identification using Image Processing Technique

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This document summarizes a research paper that proposes a system to identify vehicle seat vacancy using image processing techniques. A webcam installed in a vehicle captures passenger images and sends them to a server via 3G communication. The server then uses face detection algorithms like Viola-Jones, HOG, and CNN to detect and count faces in the images. This allows the system to calculate the vehicle's seat occupancy. The system can also estimate passengers' gender. The proposed system achieves real-time face detection and could help public transportation companies provide better customer service by displaying seat availability information.

Real time face recognition of video surveillance system using haar cascade c...

nooriasukmaningtyas

This project investigates the use of face recognition for a surveillance system. The normal video surveillance system uses in closed-circuit television (CCTV) to record video for security purpose. It is used to identify the identity of a person through their appearances on the recorded video, manually. Today’s video surveillance camera system usually not occupied with a face recognition system. With some modification, a surveillance camera system can be used as face detection and recognition that can be done in real-time. The proposed system makes use of surveillance camera system that can identify the identity of a person automatically by using face recognition of Haar cascade classifier. The hardware used for this project were Raspberry Pi as a processor and Pi Camera as a camera module. The development of this project consist of three main phases which were data gathering, training recognizer, and face recognition process. All three phases have been executed using Python programming and OpenCV library, which have been performed in a Raspbian operation system. From the result, the proposed system successfully displays the output result of human face recognition, with facial angle within ±40°, in medium and normal light condition, and within a distance of 0.4 to 1.2 meter. Targeted image are allowed to wear face accessory as long as not covering the face structure. In conclusion, this system considered, can reduce the cost of manpower in order to identify the identity of a person in real time situation.

Review on Arduino-Based Face Mask Detection System

IRJET Journal

1. The document reviews an Arduino-based face mask detection system that uses deep learning and computer vision techniques to detect faces and determine if a face mask is being worn in images and video in real-time. 2. The system is designed to help prevent the spread of COVID-19 by only allowing people wearing face masks to enter certain locations. It works by detecting faces with a camera or video feed, classifying whether a mask is detected on the face using a trained model, and then sends a signal to an Arduino device that can control access points like doors. 3. The methodology involves face acquisition from images/video, classification of faces as with or without a mask using a deep learning model, and

Face Recognition Based Payment Processing System

IRJET Journal

This document proposes a face recognition-based payment processing system. It uses facial recognition technology to identify users and process payments securely. The system works as follows: 1. A user's face is detected using Haar Cascade classifiers and their facial features are extracted using Local Binary Pattern Histograms (LBPH). 2. If the detected face matches a user in the system database, a one-time password (OTP) is sent to the user's registered mobile number. 3. Upon entering the correct OTP, the payment transaction is completed. Otherwise, the transaction fails. This system aims to provide secure contactless payments using facial biometrics for identification. It avoids the need for users to carry payment

IRJET- Vehicle Seat Vacancy Identification using Image Processing Technique

IRJET Journal

This document describes a system that uses image processing techniques on images captured by a webcam installed in a vehicle to detect passengers' faces and estimate the number of passengers and seat vacancy. The system first captures images of the passenger area and sends them to a server. It then uses techniques like morphological operations, CLAHE, and Haar classifiers for face detection. Features are extracted using LBP and HOG, and an SVM classifies images to estimate passengers' gender. Experimental results show the system can accurately detect faces and estimate numbers even from low quality images, providing real-time vacancy information to help passengers plan their travel.

The document describes a project that aims to use facial recognition as a means of contact discovery and metadata retrieval. The project seeks to optimize machine learning models for facial detection and verification in order to provide fast and accurate contact matching based on facial encodings. It outlines the objectives, scope, literature review, proposed system architecture and implementation details. The system would take facial landmarks and encodings to compare and rank the top 10 most similar encodings to identify matches from a database. The optimized model aims to reduce latency and improve accuracy for contact matching based on facial scans.

Person Acquisition and Identification Tool

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The document proposes a facial recognition system using CCTV video to identify individuals and generate timestamp data on their presence. It involves three steps: 1) face detection on video frames, 2) super resolution to standardize face sizes, and 3) face recognition using a Siamese network to identify known and new identities with one-shot learning. The system aims to reduce time spent reviewing surveillance footage for law enforcement. It analyzes existing research on low-resolution face recognition, pedestrian detection, and proposes its pipeline as a solution to semi-automate target individual tracking from video data through facial matching and timestamps.

CRIMINAL IDENTIFICATION FOR LOW RESOLUTION SURVEILLANCE

vivatechijri

Criminal Identification System allows the user to identify a certain criminal based on their biometrics. With advancements in security technology, CCTV cameras have been installed in many public and private areas to provide surveillance activities. The CCTV footage becomes crucial for understanding of the criminal activities that take place and to detect suspects. Additionallywhen a criminal is found it is difficult to locate and track him with just his image if he is on the run. Currently this procedure consists of finding such people in CCTV surveillance footage manually which is time consuming. It is also a tedious process as the resolution for such CCTV cameras is quite low. As a solution to these issues, the proposed system is developed to go through real time surveillance footage, detect and recognize the criminals based on reference datasets of criminals. The use of facial recognition for identifying criminals proves to bebeneficial. Once the best match is found the real time cropped image of the recognized criminal is saved which can be accessed by authorized officials for locating and tracking criminals or for further investigative use.

Comparative Analysis of Face Recognition Methodologies and Techniques

Farwa Ansari

In the field of computer sciences such as graphics and also analyzing the image and its processing, face recognition is the most prominent problem due to the comprehensive variation of faces and the complexity of noises and image backgrounds. The purpose and working of this system is that it identifies the face of a person from the real time video and verifies the person from the images store in the database. This paper provides a review of the methodologies and techniques used for face detection and recognition. Firstly a brief introduction of Facial Recognition is given then the review of the face recognition’s working which has been done until now, is briefly introduced. Then the next sections covered the approaches, methodologies, techniques and their comparison. Holistic, Feature based and Hybrid approaches are basically used for face recognition methodologies. Eigen Faces, Fisher Faces and LBP methodologies were introduced for recognition purpose. Eigen Faces is most frequently used because of its efficiencies. To observe the efficient techniques of facial recognition, there are many scenarios to measure its performance which are based on real time.

Saksham presentation

SakshamTurki

Computer vision can be used for many applications like facial expression detection, camera mice that move the cursor based on head movements, detecting text and defects. It allows those with limited mobility to interact with computers. Computer vision tasks include image processing, feature extraction, object detection and more. Major applications include manufacturing defect detection, barcode and text reading, and computer vision is a key technology enabling self-driving cars.

FACEMASK AND PHYSICAL DISTANCING DETECTION USING TRANSFER LEARNING TECHNIQUE

IRJET Journal

This document describes a system for detecting whether individuals are wearing face masks and maintaining proper social distancing using deep learning and computer vision techniques. The system uses the YOLOv3 object detection algorithm trained on a dataset of images labeled as containing individuals with and without masks. The trained model can detect faces in images and label them as wearing a mask properly or not. It can also detect multiple individuals, calculate distances between them, and identify violations of social distancing guidelines. The system provides outputs by drawing colored boxes around detected faces and individuals to indicate mask wearing and social distancing compliance in real-time. It aims to help authorities monitor adherence to COVID-19 safety measures without manual efforts.

A Parallel Architecture for Multiple-Face Detection Technique Using AdaBoost ...

Hadi Santoso

Face detection is a very important biometric application in the field of image analysis and computer vision. The basic face detection method is AdaBoost algorithm with a cascading Haar-like feature classifiers based on the framework proposed by Viola and Jones. Real-time multiple-face detection, for instance on CCTVs with high resolution, is a computation-intensive procedure. If the procedure is performed sequentially, an optimal real-time performance will not be achieved. In this paper we propose an architectural design for a parallel and multiple-face detection technique based on Viola and Jones' framework. To do this systematically, we look at the problem from 4 points of view, namely: data processing taxonomy, parallel memory architecture, the model of parallel programming, as well as the design of parallel program. We also build a prototype of the proposed parallel technique and conduct a series of experiments to investigate the gained acceleration.

DROWSINESS DETECTION MODEL USING PYTHON

IRJET Journal

This document describes a drowsiness detection model built using Python. The model uses computer vision and a pre-trained facial landmark detection model to identify a driver's eyes in video from a webcam. It calculates the eye aspect ratio over time to determine if the driver's eyes are closed, indicating drowsiness. If drowsiness is detected by the eyes being closed for a certain period, an alarm sound is triggered using pygame to alert the driver. The goal is to create an affordable and effective drowsiness detection system to help reduce accidents caused by tired drivers. The model was tested and successfully detected open and closed eyes and triggered alarms appropriately.

Real-time eyeglass detection using transfer learning for non-standard facial...

IJECEIAES

The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.

Deep hypersphere embedding for real-time face recognition

TELKOMNIKA JOURNAL

With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.

Person identification based on facial biometrics in different lighting condit...

IJECEIAES

Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.

Paper of Final Year Project.pdf

MuhammadAsfandyarJan1

The document describes a face recognition attendance system using computer vision techniques. It proposes using the Viola-Jones algorithm to detect faces in images and then recognize individuals based on facial features to automate attendance tracking. The system would work by detecting faces from video frames, extracting features, and matching against a database of registered users to identify and record attendees.

IRJET- Free & Generic Facial Attendance System using Android

IRJET Journal

This document proposes a free and generic facial attendance system using Android that can automatically detect students' faces and mark attendance. It uses face detection and recognition algorithms to capture images from a camera and identify students by matching faces to a database. If a face is detected, attendance is marked as present. The system then creates a Google Sheet to store and access attendance records. This provides a low-cost alternative to commercial biometric systems for tracking student attendance.

Facial Emotion Recognition: A Survey

IRJET Journal

This document provides an overview of facial emotion recognition techniques. It discusses face detection methods like Viola-Jones, multi-task cascaded convolutional neural networks (CNN), and R-CNN. For face recognition, it covers linear approaches like Eigenfaces, Fisherfaces, and Marginfaces. It also discusses CNN and HOG methods for emotion detection, like AlexNet CNN and HOG-ESRs. Popular datasets for evaluation include CK+, JAFFE, and FER2013. The document surveys key algorithms and approaches for face detection, recognition, and emotion detection.

Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...

IJERA Editor

This paper describes the technique for real time human face detection and tracking for age rank, weight and gender estimation. Face detection is involved with finding whether there are any faces in a given image and if there are any faces present, track the face and returns the face region with features of each face. Here it describes a simple and convenient hardware implementation of face detection method using Raspberry Pi Processor, which itself is a minicomputer of a credit card size. This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age evaluation based on face images. Two main components for building an efficient age estimator are facial feature extraction and estimator learning. Using feature extraction and comparing with our input database in which we have different age group face images with weight is specified according to that we also specify weight category i.e. under weight, normal weight and overweight . In this article we present gender estimation technique, which effectively integrates the head as well as mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. Facial appearance as well as head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric data from video sequences is the key approach to develop more precise and reliable realization systems.

Project presentation by Debendra Adhikari

DEBENDRA ADHIKARI

This document outlines a research project proposal for implementing real-time face recognition on an attendance system. The project aims to use machine learning and computer vision techniques to detect student faces and recognize their names for attendance tracking. The proposal discusses conducting an initial prototype using Python, OpenCV, NumPy and local binary pattern (LBP) classification. It describes collecting a database of facial images, developing the system design using use case, activity and sequence diagrams. The work plan outlines developing the prototype over several months. The goal is to gain experience with computer vision tools and apply face recognition to applications like security, banking and more.

Feature based head pose estimation for controlling movement of

IAEME Publication

This document discusses feature-based head pose estimation for controlling robot motion in real time. It begins with an abstract discussing using computer vision to efficiently estimate face orientation information in real-time to control motor movement for applications like human-computer interaction. It then reviews previous work on face and eye detection using Haar features and classifiers. The paper describes implementing Haar cascade classifiers with OpenCV to detect faces and eyes in images to estimate head pose and control a DC motor based on face movement. Key steps include face detection, eye detection using Hough transforms, and sending coordinates to a microcontroller to control the motor.

Automated Attendance Management System

IRJET Journal

This document presents a proposed automated attendance management system using face recognition. The system would use machine learning algorithms and deep learning approaches to recognize students' faces from images and track attendance. It discusses how face recognition works, including face detection, alignment, feature extraction, and recognition. It reviews similar existing systems and their limitations. The document tests several machine learning algorithms on their dataset and finds that an SVM classifier achieves the highest accuracy of 99.3%. Results are presented showing the system labeling and recognizing faces to mark attendance. The system aims to automate the attendance process to ease the burden on teachers, especially in online learning settings.

IRJET- Autonamy of Attendence using Face Recognition

IRJET Journal

This document summarizes an automated attendance system using video-based face recognition. The system works by capturing a video of students in a classroom and using face detection and recognition algorithms to identify and mark the attendance of each student. It first detects faces in each video frame using the Haar cascade classifier, then recognizes the faces by comparing them to a training database of student faces using the Eigenfaces algorithm. Finally, it registers the attendance in an Excel sheet. The system aims to make the attendance process more efficient and accurate compared to traditional manual methods.

A Literature Survey on Image Linguistic Visual Question Answering

IRJET Journal

This document discusses a literature survey on image and linguistic visual question answering. It aims to develop a model that achieves higher performance than state-of-the-art solutions by exploring different existing models and developing a custom model. The paper reviews several existing models for visual question answering and image classification using convolutional neural networks. It also discusses developing a new dataset for visual question answering using automated question generation from image descriptions.

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