scholarly journals Proposing SVM and HOG Techniques for Effective Face Recognition in Video Surveillance

2020 ◽  
pp. 805-810
Author(s):  
A. V. Deorankar ◽  
Neha S. Tadam
Keyword(s):  
Face Recognition ◽  
Computational Complexity ◽  
Video Surveillance ◽  
Image Data ◽  
Security Monitoring ◽  
The Face ◽  
Increasing Demand ◽  
And Performance ◽  
Security Monitoring System ◽  
Face Localization

Face Recognition is an active topic among Machine Learning Researchers for two decades owing to its increasing demand in security monitoring applications. The present Techniques while being working has some constraints. The challenges emerge with the orientation, quality, and expression, variations in lightning, or facial occlusions, which has a direct impact on the facial captures using video-based surveillance. This results in performance and accuracy issues. The current surveillance applications require more computational complexity with less accuracy and performance. The proposed video surveillance system overcomes these limitations of existing systems and provides maximum effective security with minimum computational complexity. The proposed Video security monitoring system provides a complete face localization, detection, and recognition. The draw out facial image data is compared with facial dataset images. The facial data is obtained from the video dataset accessed from the real environment. The face image is authenticated if a match is found and is declared unauthenticated otherwise. The security alarm after the unauthenticated alerts the security personal for further action. Hence, the proposed system is more non-evasive, accurate and reliable.

scholarly journals Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 307 ◽  
Author(s):  
Ngo Tung Son ◽  
Bui Ngoc Anh ◽  
Tran Quy Ban ◽  
Le Phuong Chi ◽  
Bui Dinh Chien ◽  
...  
Keyword(s):  
Face Recognition ◽  
Motion Blur ◽  
Security Monitoring ◽  
Flexible System ◽  
Lighting Conditions ◽  
Satisfactory Performance ◽  
Measurement Results ◽  
The Face ◽  
Recognition Systems

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

scholarly journals Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qi Han ◽  
Zhengyang Wu ◽  
Shiqin Deng ◽  
Ziqiang Qiao ◽  
Junjian Huang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Design Procedure ◽  
Biological Database ◽  
Model Parameters ◽  
Face Database ◽  
Recognition Method ◽  
The Face ◽  
Model Training ◽  
And Performance ◽  
The Stability

In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

scholarly journals Encrypted face recognition algorithm based on Ridgelet-DCT transform and THM chaos

2021 ◽  
Vol 19 (2) ◽  
pp. 1373-1387
Author(s):  
Zilong Liu ◽  
◽  
Jingbing Li ◽  
Jing Liu ◽  
◽  
...  
Keyword(s):  
Face Recognition ◽  
Homomorphic Encryption ◽  
Image Data ◽  
Recognition Algorithm ◽  
Personal Privacy ◽  
Dct Transform ◽  
Face Images ◽  
Potential Safety ◽  
The Face ◽  
Encryption Method

<abstract> <p>With the popularization and application of face recognition technology, a large number of face image data are spread and used on the Internet. It has brought great potential safety hazard for personal privacy. Combined with the characteristics of tent chaos and Henon chaos, a THM (tent-Henon map) chaotic encrypted face algorithm based on Ridgelet-DCT transform is proposed in this paper. Different from conventional face recognition methods, this new approach encryptes the face images by means of using the homomorphic encryption method to extract their visual robust features in the first place, and then uses the proposed neural network model to design the encrypted face recognition algorithm. This paper selects the ORL face database of Cambridge University to verify the algorithm. Experimental results show that the algorithm has a good performance in encryption effect, security and robustness, and has a broad application prospect.</p> </abstract>

scholarly journals The Design and Implementaion of PTZ Control in Intelligent Video Surveillance

2013 ◽  
Vol 347-350 ◽  
pp. 1961-1964
Author(s):  
Sai Dong Lv ◽  
Guo Hua Tang ◽  
Yao Wen Xia ◽  
Ji Li Xie
Keyword(s):  
Monitoring System ◽  
Video Surveillance ◽  
Serial Communication ◽  
System Structure ◽  
Main Function ◽  
Intelligent Video Surveillance ◽  
Security Monitoring ◽  
Security Monitoring System

In the security monitoring system, in order to realize the multi-angle and different positions of the camera monitoring the camera monitoring, a pan tilt zoom (PTZ) module is used. This paper describes the PELCO-D protocol of PTZ and serial communication firstly. Then it achieves control the of PTZ and lens work based on Windows VC++. Finally, It discusses the main function of the system structure and coding.

Facial-recognition algorithms: A literature review

2020 ◽  
Vol 60 (2) ◽  
pp. 131-139
Author(s):  
Paramjit Kaur ◽  
Kewal Krishan ◽  
Suresh K. Sharma ◽  
Tanuj Kanchan
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Surveillance Systems ◽  
Person Identification ◽  
Security Monitoring ◽  
Advantages And Disadvantages ◽  
Potential Applications ◽  
The Face ◽  
Automated Surveillance ◽  
Large Groups

The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accepted biometric method. The domain of face recognition has gained the attention of many scientists, and hence it has become a standard benchmark in the area of human recognition. It has turned out to be the most deeply studied area in computer vision for more than four decades. It has a wide array of applications, including security monitoring, automated surveillance systems, victim and missing-person identification and so on. This review presents the broad range of methods used for face recognition and attempts to discuss their advantages and disadvantages. Initially, we present the basics of face-recognition technology, its standard workflow, background and problems, and the potential applications. Then, face-recognition methods with their advantages and limitations are discussed. The concluding section presents the possibilities and future implications for further advancing the field.

scholarly journals Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah

2016 ◽  
Vol 6 (1) ◽  
pp. 30
Author(s):  
Nahdi Sabuari ◽  
Rizal Isnanto ◽  
Kusworo Adi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Image Data ◽  
Training Algorithm ◽  
Accuracy Rate ◽  
Average Accuracy ◽  
The Face ◽  
Artificial Neural

This research discusses about face detection and face recognition in an image. Face detection has only two classifications, i.e face and not face. Face recognition is compatible with some classifications of a number individuals who want to be recognized. Face detection and face recognition in thi study using Haar-Like Feature method and Artificial Neural Network Backpropagation. A method Haar-Like Feature used for detection and extraction in an image, because the clasification on this method showed success at used to detect image of the face. Artificial Neural Network Backpropagation is a training algorithm that is used to do training simulated on facial image data training stored in a database. This study uses Ms. Excel 2007 as database with 10 individual sample image, every image in each individuals having three distance with every range has four defferent light intensities, so that the data training stored in the database reached 120 data training. The results shows that the face detection and face recognition which is developed can recognize a face image with an average accuracy rate reaches 80,8% for each distance.

Fast Non-Negative Matrix Factorizations for Face Recognition

2017 ◽  
Vol 32 (04) ◽  
pp. 1856003 ◽  
Author(s):  
Wen-Sheng Chen ◽  
Yugao Li ◽  
Binbin Pan ◽  
Chen Xu
Keyword(s):  
Face Recognition ◽  
Gradient Descent ◽  
Image Data ◽  
Data Representation ◽  
Superior Performance ◽  
Special Cases ◽  
Fast Gradient ◽  
And Performance ◽  
Sparse Features ◽  
Non Negative Matrix Factorization

Non-negative Matrix Factorization (NMF), as a promising image-data representation approach, encounters the problems of slow convergence and weak classification ability. To overcome these limitations, this paper, based on different error measurements, proposes two kinds of NMF algorithms with fast gradient descent and high discriminant performance. It is shown that the proposed Fast NMF (FNMF) methods have larger step sizes than those of traditional NMFs. Moreover, the traditional NMFs are the special cases of our methods. To further enhance the discriminative power of non-negative features, we exploit our previous block NMF technique and obtain Block FNMF (BFNMF) algorithms, which are supervised decomposition approaches with some good properties, such as the highly sparse features and orthogonal features from different classes. In experiments, both convergence on non-negative decomposition and performance on face recognition (FR) are considered for evaluations. Compared with traditional NMF algorithms and some state-of-the-art methods, experimental results indicate the effective and superior performance of the proposed NMF methods.

Deep Learning Model for Face Recognition in Unconstrained Environment

2019 ◽  
Vol 16 (10) ◽  
pp. 4309-4312
Author(s):  
Rajeshwar Moghekar ◽  
Sachin Ahuja
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Transfer Learning ◽  
Face Database ◽  
Security Monitoring ◽  
Face Images ◽  
Feature Extractor ◽  
The Face ◽  
Deep Learning Model ◽  
Fine Tune

Face recognition from videos is challenging problem as the face image captured has variations in terms of pose, Occlusion, blur and resolution. It has many applications including security monitoring and authentication. A subset of Indian Movies Face database (IMFDB) which has collection of face images retrieved from movie/video of actors which vary in terms of blur, pose, noise and illumination is used in our work. Our work focuses on the use of pre-trained deep learning models and applies transfer learning to the features extracted from the CNN layers. Later we compare it Fine tuned model. The results show that the accuracy is 99.89 using CNN as feature extractor and 96.3 when we fine tune the VGG-Face. The Fine tuned network of VGG-Face learnt more generic features when compared with its counterpart transfer learning. When applied on VGG16 transfer learning achieved 93.9.

Intelligent Video Analysis System Based on ARM Cortex

2014 ◽  
Vol 556-562 ◽  
pp. 3216-3218
Author(s):  
Kai Zhang
Keyword(s):  
Monitoring System ◽  
Video Surveillance ◽  
Video Analysis ◽  
Intelligent Video Surveillance ◽  
Security Monitoring ◽  
Analysis System ◽  
Security Monitoring System

With the development of the society, security monitoring system more and more get people's attention. Article designs the intelligent video analysis technology based on ARM architecture on the basis of Intelligent Video Surveillance, and discusses the application prospect of intelligent video analysis system.

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