Real-time Face Recognition System Research Articles

Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

Neural Networks (CNNs) and Vgg on Real Time Face Recognition System

Face Recognition is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. This work introduces a new real time face Recognition system. The process is broken into four major steps: (1) database collection, (2) face recognition to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92.Second step, efficient real time face recognition algorithm is then used to recognize faces with a known database. For real time face Recognition, VGG-16 with Transfer Learning and Convolutional Neural Network (CNN) are used. This proposed system is implemented using keras. Lastly the performance of these two classifiers is measured using of precision, recall, F1-score, and accuracy.

Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

RETRACTED: Unity 3D animation modeling based on machine vision and embedded system

It proposes a reliable method for estimating a single image, the three-dimensional pose of the human head. As opposed to different examinations, this technique utilizes data about just the skin and hair regions. To begin with, utilize an outwardly uniform shading framework and Neural Network (NN) to extricate the skin and hair territories, and afterward, it utilizes a productive calculation to recognize the appearances in the picture. The focal point of the region is then used to ascertain in any event the inertial tomahawks of both the skin and hair territories and to gage the three-dimensional stance of the head. It tried our technique by assessing the three-dimensional stance of the head from a live video grouping. It is face detection and recognition recognizes, presents a complete real-time face recognition system, and uses an FPGA sampling module. The robustness and reliability of this technique are significantly improved compared to those based on tracking facial features. This is because extract such stability and use global information as areas, centers, and other related skin and hair areas that can be insensitive to local changes with facial features. It utilizes an exceptionally incredible skin and hair zone recognition calculation contrasted with other announced strategies utilized because our stance assessment calculation doesn't utilize any 3D head. Likewise, this technique is more dependable and simpler to use for skin areas model and camera parameters.

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30 degrees on either side from the front face showed an average face recognition precision of 72.25% - 81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

A Real-time Face Tracking and Recognition System Based on SeetaFace

A real-time face tracking and recognition system is constructed based on Seetaface, and it is proposed to use Gamma correction to reduce the impact of illumination changes on the detection and recognition results. First, the system has face detection function, then by using OpenCV to get the image in front of the camera, the system completes the function of real-time face tracking, users can also upload local images to make face comparison. If a stranger’s face is detected, the system can collect information and train it. The system achieves multiple face tracking as well as recognition. Through the analysis and evaluation on public datasets. The improved face tracking and recognition system has a satisfactory accuracy and performance.

FACIAL RECOGNITION SYSTEM USING IMAGE PROCESSING

Authentication is a major challenge in computer-based communication system operation. Human face recognition is an important branch of biometric verification and has been commonly used in many applications, including video monitoring system, human-computer interaction, door control system, and network protection. A computational neural network can be implemented as a framework in the face classification process. In order to recognize dimensions within the intend-able limits, the test system offers acceptable performance. The device also allows multiple faces to be identified and recognized in live pictures. Using OpenCV and programming with Python, we design a real-time face recognition system.

Active Discriminative Cross-Domain Alignment for Low-Resolution Face Recognition

In real application scenarios, the face images captured by cameras often incur blur, illumination variation, occlusion, and low-resolution (LR), which leads to a challenging problem for many real-time face recognition systems due to a big distribution difference between the captured degraded images and the high-resolution (HR) gallery images. As widespread application of transfer learning in across-visual recognition, we propose a novel active discriminative cross-domain alignment (ADCDA) technique for LR face recognition method by jointly exploring both geometrical and statistical properties of the source domain and the target domain in a unique way. Specifically, the proposed ADCDA-based method contains three key components: 1) it simultaneously reduces the domain shift in both marginal distribution and conditional distribution between the source domain and the target domain; 2) it aligns the data of two domains in the common latent subspace by discriminant locality alignment (DLA); 3) it selects the representative and the diverse samples with an active learning strategy to further improve classification performance. Extensive experiments on six benchmark databases verify that the proposed method significantly outperforms other state-of-the-art predecessors.

Design and Evaluation of a Real-Time Face Recognition System using Convolutional Neural Networks

Abstract The advent of high speed processors and high resolution cameras has spearheaded the research towards design of face recognition systems for various applications. Face recognition systems use either offline data or real-time input, based on the application. In this paper, design and evaluation of a real-time face recognition system using Convolutional Neural Network (CNN) is proposed. The initial evaluation of the proposed design is carried out using standard AT&T datasets and the same is later extended towards the design of a real-time system. Details about the tuning of CNN parameters to assess and enhance the recognition accuracy of the proposed system are also reported. A systematic approach to tune the parameters is also proposed to enhance the performance of the system. Maximum recognition accuracies of 98.75% and 98.00% are obtained on using the proposed system with standard datasets and real-time inputs respectively.

Prediction based person recognition using face and speech (multi modal) for improved performance

In recent World Technological Applications Person Recognition plays a major role in biometric security applications and it is a process of authenticating true identity of a speaker using speech or face image. This automatically recognizes the person speaking based on the speech information which includes the individual speech signals. It’s one of applications is Bio-metric applications and in order to verify each person identity, the speaker’s voice or face images have been used in the database. The importance of it becoming more popular now-a-days for security purpose and identification. In the existing work using visualization eye scan, impressions, expression scan, finger print, speech print, script for individuals of identifying the chances of theft and fraud are increasing. To address these issues, biometric voice recognition and real time face recognition system are proposed, the exclusive speaker physical appearance of a distinct can be identified. In general the individuals have different speed of speaking; therefore the sound should be adjusted, in order to match with the speed of the stored sounds templates in the memory of the proposed system. The proposed work is implemented and simulated using on Mat lab 2014A. The parallel hardware structure of the proposed work significantly reduces the time-consumption. The proposed research work provides maximum False Acceptance Rate (FAR) of 1.1765%, False Rejection Rate of 10% with an accuracy of 98.89%.

CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition

In recent years, face recognition has become more and more appreciated and considered as one of the most promising applications in the field of image analysis. However, the existing models have a high level of complexity, use a lot of computational resources and need a lot of time to train the model. That is why it has become a promising field of research where new methods are being proposed every day to overcome these difficulties. We propose in this paper a convolutional neural network system for face recognition with some contributions. First we propose a CRelu module, second we use the module to propose a new architecture model based on the VGG deep neural network model. Thirdly we propose a two stage training strategy improved by a large margin inner product and a small dataset and finally we propose a real time face recognition system where face detection is done by a multi-cascade convolution neural network and the recognition is done by the proposed deep convolutional neural network.

Real-time ensemble based face recognition system for NAO humanoids using local binary pattern

NAO humanoid robots are being used in many human-robot interaction applications. One of the important existing challenges is developing an accurate real-time face recognition system which does not require to have high computational cost. In this research work a real-time face recognition system by using block processing of local binary patterns of the face images captured by NAO humanoid is proposed. Majority voting and best score ensemble approaches have been used in order to boost the recognition results obtained in different colour channels of YUV colour space, which is a default colour space provided by the camera of NAO humanoid. The proposed method has been adopted on NAO humanoid and tested under real-world conditions. The recognition results were boosted in the real-time scenario by employing majority voting on the intra-sequence decisions with window size of 5. The experimental results are showing that the proposed face recognition algorithm overcomes the conventional and state-of-the-art techniques.

A Kinect-Based Wearable Face Recognition System to Aid Visually Impaired Users

In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.

Portable Facial Recognition Jukebox Using Fisherfaces (Frj)

A portable real-time facial recognition system that is able to play personalized music based on the identified person’s preferences was developed. The system is called Portable Facial Recognition Jukebox Using Fisherfaces (FRJ). Raspberry Pi was used as the hardware platform for its relatively low cost and ease of use. This system uses the OpenCV open source library to implement the computer vision Fisherfaces facial recognition algorithms, and uses the Simple DirectMedia Layer (SDL) library for playing the sound files. FRJ is cross-platform and can run on both Windows and Linux operating systems. The source code was written in C++. The accuracy of the recognition program can reach up to 90% under controlled lighting and distance conditions. The user is able to train up to 6 different people (as many as will fit in the GUI). When implemented on a Raspberry Pi, the system is able to go from image capture to facial recognition in an average time of 200ms.

An Efficient Face Recognition using Feature Filter and Subspace Projection Method

Purpose : In this paper we proposed cascade feature filter and projection method for rapid human face recognition for the large-scale high-dimensional face database. Materials and Methods : The relevant features are selected from the large feature set using Fast Correlation-Based Filter method. After feature selection, project them into discriminant using Principal Component Analysis or Linear Discriminant Analysis. Their cascade method reduces the time-complexity without significant degradation of the performance. Results : In our experiments, the ORL database and the extended Yale face database b were used for evaluation. On the ORL database, the processing time was approximately 30-times faster than typical approach with recognition rate 94.22% and on the extended Yale face database b, the processing time was approximately 300-times faster than typical approach with recognition rate 98.74 %. Conclusion : The recognition rate and time-complexity of the proposed method is suitable for real-time face recognition system on the large-scale high-dimensional face database.

Face Recognition using Eigenfaces

We tried to develop a real time face detection and recognition system which uses an “appearance-based” approach. For detection purpose we used Viola Jones algorithm. To recognize face we worked with Eigen Faces which is a PCA based algorithm. In a real time to recognize a face we need a data training set. For data training set we took five images of each person and manipulated the Eigen values to match the known individual.

A Near-Infrared Face Detection and Recognition System Using ASM and PCA+LDA

Near-infrared (NIR) images have a lot of advantages, which can make up the shortages of visible light (VL) images. A novel NIR detection and recognition system is presented in this paper. First, a NIR imaging system is developed to provide good illumination conditions for subsequent face detection and recognition. Then, Active Shape Model (ASM) method is applied to detect the features and the low-dimensional Gabor features are extracted for recognition by combining Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The recognition rate of the system is up to 90% in a few milliseconds. Finally, by using such real-time NIR face recognition system, comparative results are provided, from that we can seen that this system can work robustly and effectively

Based on the Adaboost Algorithm of Face Detection Research

Because of the influence of complex image background, illumination changes, facial rotation and some other factors, makes face detection in complex background is much more difficult, lower accuracy and slower speed. Adaboost algorithm was used for face detection, and implemented the test process in OpenCV. Face detection experiments were performed on images with facial rotation and complex background, the detection accuracy rate was 85% and 99% respectively, the average detection time of each picture was 16.67ms and 76ms.Experimental results show that the face detection algorithm can accurately and quickly realize face detection in complex background, and can satisfy the requirements of real-time face recognition system.

Design and Implementation of Real Time Face Recognition System (RTFRS)

recognition is a pattern recognition technique and one of the most important biometrics; it is used in a broad spectrum of applications. The accuracy is not a major problem that specifies the performance of automatic face recognition system alone, the time factor is also considered a major factor in real time environments. Recent architecture of the computer system can be employed to solve the time problem, this architecture represented by multi-core CPUs and many- core GPUs that provide the possibility to perform various tasks by parallel processing. However, harnessing the current advancements in computer architecture is not without difficulties. Motivated by such challenge, this paper proposes a Real Time Face Recognition System (RTFRS). In doing so, this paper provides the architectural design, detailed design, and four variant implementations of the RTFRS. Finally, this paper determines the speed up obtained for the three advanced implementations (i.e., Hybrid Parallel model, CPU Parallel model, and Hybrid Mono model) against the convention implementation (i.e., CPU Mono model). The practical results demonstrate that the Hybrid Parallel model gain highest speed up around 82X, CPU Parallel model also have a high speed up around 71X, and finally, the Hybrid Mono model gives a slight speed up about 1.04X.

Generalized Weber-face for illumination-robust face recognition

Robust face recognition under uncontrolled illumination conditions is one of the key challenges for real-time face recognition systems. Weber-face (WF) is an illumination insensitive face representation based on Weber׳ law. In this letter, we develop a generalized Weber-face (GWF) which extracts the statistics of multi-scale information from face images. By assigning different weights to the inner-ground and outer-ground we further develop a weighted GWF (wGWF) version. Based on our experiments on the extended Yale-B and FERET face database we show that the proposed methods are robust to illumination variations and can obtain promising performance comparable with existing approaches.