Video Face Recognition Research Articles

Deep fake Video Face Recognition Using Supervised Contrastive Learning for Scalability and Interpretability

Deep fake Video Face Recognition Using Supervised Contrastive Learning for Scalability and Interpretability

Nonlinear dynamical system iteration applied in video face feature extraction and recognition

Nonlinear dynamical system iteration applied in video face feature extraction and recognition

HUMAN FACE RECOGNITION ON IMAGE VIDEO CONFERENCE APPLICATION USING SIAMESE NETWORK WITH SKIP CONNECTION SMALLER VGG MODEL

Attendance recording is needed to find out someone's attendance at a meeting or meeting. These meetings are sometimes conducted online through the video conferencing application. Recording attendance at online meetings is using an online form that is distributed via chat. There are several problems such as chats piling up and meeting participants arriving late so they cannot access the form link. Therefore, facial recognition can be used to record attendance using screenshots as an attendance record with the aim of helping to facilitate attendance recording through video conferencing applications using computer vision technology. This study proposes a method of using the Siamese network with the Smaller VGG skip connection model to improve human face recognition in video conferencing application images. Has validation accuracy results in the training phase of 98%, precision of 98%, and recall of 98%. For the similarity phase where the model is applied to the Siamese network, the accuracy is 95%, the precision is 53%, and the recall is 78%. Then the model is applied to the pipeline system with the YOLO-face model to classify the results of face detection from Yolo with the faces in the database so that the model does not need to be retrained if there are new faces, it only needs to add facial images to the database to be compared with the query image..

Transfer learning-based security protection framework for smart exercise monitoring

Sports exercise is very important for both physical and mental health, but improper exercise or equipment use may cause some security challenges. To address this issue, facing Internet of Things (IoT), currently, some video recognition algorithm-based smart security monitoring systems have been designed. However, existing video recognition algorithms usually assume that the data comes from the same collection device or follows the same distribution, resulting in ineffective handling of cross-camera or cross-device recognition problems. In this vein, this paper designed a transfer learning-based smart exercise monitoring security protection system, and proposed a new transfer learning-based video recognition framework, which consists of backbone network module, style transfer module, video feature aggregation module three parts and using this framework, two different models can be trained based on video face recognition dataset and video action recognition dataset, respectively, for identity recognition and action recognition. Experimental results show that the proposed framework can effectively handle video face recognition and video action recognition problems, which also demonstrates that our designed smart exercise monitoring security protection system can meet actual task requirements.

Create and implement a new method for robust video face recognition using convolutional neural network algorithm

Biometric applications have massive demand in today's era. The areas of applications are mostly linked with the security of the system. Biometric features are regarded as the primary resource for security purposes due to their own distinctiveness and non-volatile essence. System authentication using biometrics is considered to be a sophisticated technology. Noise effect inducts variation in the biometric subject that causes an adverse impact on establishing the recognition. The proposed model supported the development of an effective method for performing facial biometric feature recognition. The model's goal is to reduce the number of false approvals and refusals. The proposed algorithm has been applied over a video dataset containing surveillance video frames that captures facial subjects dynamically. The first step is pre-processing of the video frames that have been carried out in the proposed model. Then, the Viola-Jones algorithm was applied to detect the facial subjects in the video frames. Feature extraction from the facial subject has been accomplished by applying a deep reinforcement learning algorithm. Further, the proposed model applied a convolutional neural network (CNN) algorithm to perform feature recognition of facial identity accurately. The proposed technique aims to maintain a huge recognition rate of dynamic facial subjects under various unprecedented noise variations. In the classification algorithm, the recognition accuracy is found to be 98.85%.

A novel sketch based face recognition in unconstrained video for criminal investigation

Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.

Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems.

FRMDB: Face Recognition Using Multiple Points of View.

Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.

A feature map aggregation network for unconstrained video face recognition

Unconstrained video face recognition is an extension of face recognition technology, and it is an indispensable part of intelligent security and criminal investigation systems. However, general face recognition technology cannot be directly applied to unconstrained video face recognition, because the video contains fewer frontal face image frames and a single image contains less face feature information. To address the above problems, this work proposes a Feature Map Aggregation Network (FMAN) to achieve unconstrained video face recognition by aggregating multiple face image frames. Specifically, an image group is used as the input of the feature extraction network to replace a single image to obtain a multi-channel feature map group. Then a quality perception module is proposed to obtain quality scores for feature maps and adaptively aggregate image features from image groups at the feature map level. Finally, extensive experiments are conducted on the challenging face recognition benchmarks YTF, IJB-A and COX to evaluate the proposed method, showing a significant increase in accuracy compared to the state-of-the-art.

A Real-Time Face Detection Method Based on Blink Detection

Aiming at the photo fraud that often occurs in identity verification and the accuracy and robustness of real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. This method first extracts the image texture features through the LBP algorithm, which eliminates the problem of illumination changes to a certain extent. Then the extracted features are input into the ResNet network, and the facial feature extraction is enhanced by adding an attention mechanism is added to enhance the face feature extraction. Meanwhile, the BiLSTM method is used to extract the temporal characteristics of images from different angles or at different times to obtain more facial details. In addition, the fusion of local and global features is realized by SPP pooling, which enriches the expression ability of feature maps and improves detection accuracy. Finally, the eye EAR value is calculated by the face key point detection technology to achieve face anti-spoofing, and then the real-time face recognition against fraud is realized. The experimental results show that the algorithm proposed in this paper has good accuracy on NUAA, CASIA-SURF and CASIA-FASD datasets, which can reach 99.48%, 98.65% and 99.17%, respectively.

Tracking Missing Person in Large Crowd Gathering Using Intelligent Video Surveillance.

Locating a missing child or elderly person in a large gathering through face recognition in videos is still challenging because of various dynamic factors. In this paper, we present an intelligent mechanism for tracking missing persons in an unconstrained large gathering scenario of Al-Nabawi Mosque, Madinah, KSA. The proposed mechanism in this paper is unique in two aspects. First, there are various proposals existing in the literature that deal with face detection and recognition in high-quality images of a large crowd but none of them tested tracking of a missing person in low resolution images of a large gathering scenario. Secondly, our proposed mechanism is unique in the sense that it employs four phases: (a) report missing person online through web and mobile app based on spatio-temporal features; (b) geo fence set estimation for reducing search space; (c) face detection using the fusion of Viola Jones cascades LBP, CART, and HAAR to optimize the results of the localization of face regions; and (d) face recognition to find a missing person based on the profile image of reported missing person. The overall results of our proposed intelligent tracking mechanism suggest good performance when tested on a challenging dataset of 2208 low resolution images of large crowd gathering.

Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition

Deep Learning approaches have brought solutions, with impressive performance, to general classification problems where wealthy of annotated data are provided for training. In contrast, less progress has been made in continual learning of a set of non-stationary classes, mainly when applied to unsupervised problems with streaming data.Here, we propose a novel incremental learning approach which combines a deep features encoder with an Open-Set Dynamic Ensembles of SVM, to tackle the problem of identifying individuals of interest (IoI) from streaming face data. From a simple weak classifier trained on a few video-frames, our method can use unsupervised operational data to enhance recognition. Our approach adapts to new patterns avoiding catastrophic forgetting and partially heals itself from miss-adaptation. Besides, to better comply with real world conditions, the system was designed to operate in an open-set setting. Results show a benefit of up to 15% F1-score increase respect to non-adaptive state-of-the-art methods.

Face tracking and recognition in video moving images based on convolutional neural network

Face tracking and recognition in video moving images based on convolutional neural network

Fast and reliable probabilistic face embeddings based on constrained data uncertainty estimation

Probabilistic Face Embeddings (PFE) can improve face recognition performance in unconstrained scenarios by integrating data uncertainty into the feature representation. However, the matching speed of PFE is too slow to be applied to large-scale face recognition or retrieval applications. Moreover, since deep learning-based data uncertainty estimation tends to be over-confident, the recognition performance of PFE is unstable. This paper proposes a probabilistic face embedding method to improve the robustness and speed of PFE. Specifically, the mutual likelihood score (MLS) metric used in PFE is simplified by using a one-dimensional variance to approximate the data uncertainty of face feature, to speed up the matching of probabilistic embedding pairs. Then, a unilateral constraint loss is proposed to limit the variation range of the lower part of the estimated data uncertainties, which can solve the problem of accuracy degradation in high-quality images. In addition, a feature fusion method based on temperature scaling is proposed, which can adjust the fusion weights of different quality images to improve the performance of video face recognition. Comprehensive experiments show that the proposed method can achieve comparable or better results in 6 benchmarks than the state-of-the-art methods with a less computational cost at the matching process. The code of our work is publicly available in GitHub (https://github.com/KaenChan/ProbFace).

Video Face Detection Technology and Its Application in Health Information Management System

Computer face detection, as an early step and prerequisite for applications such as face recognition and face analysis, has attracted people's attention for a long time. With the popularization of computer applications, the improvement of performance, and the gradual maturity of research in the field of image processing and pattern recognition, face-related applications have become more and more a reality, so the research on face detection and positioning is also receiving more and more attention. Face detection and positioning are an important part of face analysis technology. Its goal is to search for the location of facial features (such as eyes, nose, mouth, and ears) in images or image sequences. It can be widely used in the fields of face tracking, face recognition, gesture recognition, facial expression recognition, head image compression and reconstruction, facial animation, etc. Based on the health information management system, this study mainly discusses the application of face recognition technology in video systems. Compared with other biological characteristics, such as fingerprints and eye masks, human faces are easier to obtain. In research and exploration, stable and effective face detection and face recognition algorithms have been proposed, which can achieve good recognition results even in real-time video surveillance. Aiming at the automatic face recognition technology in video surveillance, this study introduces in detail the video face detection technology in the health information management system of video image collection, image preprocessing, face detection, and face recognition. The prototype system of hygiene management is recognized.

Face detection and recognition in organic video: A comparative study for sport celebrities database

The present paper reports on an experimental study carried out under the applicative field of organic video processing, and relates to the possibility of identifying sport celebrities (soccer players) in video content. In contrast to common state-of-the-art studies, a special attention is paid on the cases in which the face is not completely included in the frame (lateral views, partial occlusions, etc.) and/or in which arbitrarily lighting conditions occur. To this aim, we consider two conventional types of face detection algorithms (Haar Cascade Classifier, and MMOD - Max-Margin object detection) coupled to two conventional face recognition models (LSBH - Local binary pattern histogram, and CNN-based Pruned ResNet). The experimental work consists of evaluating the end-to-end performances of the four possible combinations among the above-mentioned two face detection and two face recognition methods. A database of 20 video sequences of about 3 minutes each is organized. As an overall conclusion, we brought to light that the MMOD coupled to a Pruned ResNet model seems to better suit the organic video processing use-case constraints, being able to reach a recognition rate of 98%.

Research on video image face detection and recognition technology based on improved MTCNN algorithm

With the development of modern computer technology and artificial intelligence, face image processing technology has been widely used in people's life and work. In order to realise face image detection and recognition in dynamic video, this paper proposes a face detection and recognition technology based on MTCNN algorithm. MTCNN algorithm includes R-Net, O-net and P-net deep network models, which can realise face image deep processing in dynamic video. In order to train MTCNN algorithm deeply, Wider_Face and CelebA database training sets were used to train the additional test tasks and regression key points of the model. After setting the main parameters of MTCNN algorithm, the algorithm is simulated and analysed. Through the comparative simulation analysis of traditional algorithm, SVM algorithm and 2DPCA algorithm, it can be seen that MTCNN algorithm has more excellent performance and can meet the needs of face image detection and recognition in dynamic video.

Context-Aware Contribution Estimation for Feature Aggregation in Video Face Recognition

The difficulties in video-based face recognition, such as dramatic pose variations and low quality, can be alleviated by leveraging the rich complementary information between the frames. However, limited by the mini-batch training strategy, the current deep learning methods only utilizes the frames in each batch during training, which ignore the context of the entire video. In this paper, we propose a context-aware feature aggregation scheme to aggregate complementary information between different frames. Firstly, a two-branch structure is designed as the Context-aware feature Aggregation Network (CAN). Secondly, a context-aware training strategy using context bank is proposed, which alleviates the limitation of mini-batch samples by using the context of the entire video or several images belonging to the same ID and thus achieves global contribution estimation result. Comparative studies on benchmark datasets, such as IJB-C, YouTube Face (YTF), PaSC and COX, confirm that the proposed approach can achieve state-of-the-art level. Meanwhile, qualitative analysis on Multi-PIE dataset indicates that the contribution learned by the CAN is reasonable and beneficial to video face recognition.

Eye of Devil: Face Recognition in Real World Surveillance Video with Feature Extraction and Pattern Matching

Abstract: People lost, people missing etc., these are the words we come across whenever there is any mass gathering events going on or in crowded areas. To solve this issue some traditional approaches like announcements are in use. One idea is to identify the person using face recognition and pattern matching techniques. There are several techniques to implement face recognition like extraction of facial features by using the position of eyes, nose, jawbone or skin texture analysis etc., By using these techniques a unique dataset can be created for each human. Here the photograph of the missing person can be used to extract these facial features. After getting the dataset of that individual, by using pattern matching techniques, there is a scope to find the person with same facial features in the crowd images or videos. Keywords: Face-Recognition, Image-Processing, Feature extraction, Video-Processing, Pattern-Matching.

Deep Learning for Convolution Neural Networks and Recurrent Neural Networks: A Review

Many with computational models that allow deep learning representations to learn to make processing layers. Summary of multiple levels with information. These methods have dramatically improved many fields like speech recognition, visual material detection, substance recognition and drug discovery, and genetics. (1) Deep machine learning uses back-propagation algorithms to understand how the wild machine detects complexity structures in large data sets. Deep transformation networks exist advances have come in image processing, video, speech, and sound, while continuous networks. Deep learning is the latest, cutting-edge technology Image processing and data analysis, reliable results and large capacity. Deep learning has been successfully used in various domains, including recently agricultural sector as well. In this paper, we review 40 research initiatives using Deep learning techniques are applied to various agricultural and food production challenges. (2) We examine specific agricultural study problems, the specific models and frameworks used as sources, the nature and data pre processing used, and the metrics used to measure overall performance as stated of each study task. A subset of deep learning algorithms are machine learning algorithms that are distributed representations with multilevel detection. Recently, contribute challenges to more than 210 recent research papers. (3) This book describes advances in implementing deep neural networks for automatic feature extraction in applications including image and video face recognition, programmatic video highlighting, and image segmentation and object classification. (4)Image recognition is a popular idea in transfer learning and its beginnings. Basically pre-trained models can be some type of reproducible classification applications using images on small datasets. We practice different architectures learning only a small number of calculations weights and residuals. Ideally, the network's weights can predict 95% accuracy without loss. (5) Apart from these and other findings, in this article, the authors present define surface and deep learning and describe observed system. Learning outcome taxonomy is used to assess depth.