scholarly journals Person Re-Identification using Reduced Dictionary Sparse Representation Based Classifier

2019 ◽  
Vol 8 (4) ◽  
pp. 9503-9507
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Video Surveillance ◽  
Decomposition Method ◽  
Similarity Score ◽  
Surveillance Video ◽  
Illumination Variation ◽  
Public Places ◽  
Basis Expansion ◽  
Lighting Conditions

Video surveillance has become a necessary tool for monitoring a public places. Automation of the video surveillance is the current need, as it a tough task for the humans to monitor the surveillance video continuously. Also searching a person from a bulk of videos is not an easy job. Person re-identification is a step taken towards to make the video surveillance an automated one. Person re-Identification is a task of matching the identity of a person captured by different cameras in the network at different places and times. The cameras used for surveillance are located at a much higher position than the person so that the conventional method of face recognition is not used for identification of the person. The images of the same person may differ based on the qualities of different cameras (resolution changes), or due to different lighting conditions (variation in illumination) or due to posture changes. Recently sparse based classifier is used in person re-identification and is effective in handling in illumination variation and occlusion. But sparse based decomposition method is not computational efficient. This leads to the development of the person re-identification method based on Reduced Dictionary Sparse representation based Classifier (RDSC). This is done with 2 steps: (i) Similarity score for reduction of the gallery; (ii) Sparse basis expansion of targets in terms of reduced gallery. The proposed method is both computational efficient and creates better outcomes.

Privacy Preserving in Video Surveillance Systems Using Regression Residual Convolutional Neural Network in Private and Public Places

2020 ◽  
Vol 12 (1) ◽  
pp. 39-55
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Security Policy ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Public Places ◽  
Private And Public ◽  
The One ◽  
Regression Residual

In recent years, surveillance video has become a familiar phenomenon because it gives us a feeling of greater security, but we are continuously filmed and our privacy is greatly affected. This work deals with the development of a private video surveillance system (PVSS) using regression residual convolutional neural network (RR-CNN) with the goal to propose a new security policy to ensure the privacy of no-dangerous person and prevent crime. The goal is to best meet the interests of all parties: the one who films and the one who is filmed.

scholarly journals Dense Feature Based Face Recognition from Surveillance Video using Convolutional Neural Network

2021 ◽  
Vol 12 (5) ◽  
pp. 1436-1449
Author(s):  
K. Suma, Et. al.
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Local Binary Pattern ◽  
Surveillance Video ◽  
Human Computer Interactions ◽  
Illumination Variation ◽  
Application Range ◽  
Feature Based ◽  
Pose Variation

Face Recognition is a field of identifying the person from the facial features and has wide application range in security, human computer interactions, finance etc. In recent years, many researchers have developed different algorithms to identify the Faces from various illumination variations and Pose variation, but these two problems remain unsolved in Face Recognition (FR) field. The Local Binary Pattern (LBP) has already proved its robustness in illumination variation. This paper proposes a four-patch Local Binary Pattern based FR utilizing Convolutional Neural Network (CNN) for identifying the Facial images from various illumination conditions and Pose variation.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

Double layers sparse representation for occluded face recognition

Optik ◽  
2015 ◽  
Vol 126 (21) ◽  
pp. 3016-3019 ◽  
Author(s):  
Shuhuan Zhao ◽  
Zheng-ping Hu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Double Layers

A novel sparse representation method based on virtual samples for face recognition

2012 ◽  
Vol 24 (3-4) ◽  
pp. 513-519 ◽  
Author(s):  
Deyan Tang ◽  
Ningbo Zhu ◽  
Fu Yu ◽  
Wei Chen ◽  
Ting Tang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Virtual Samples ◽  
Representation Method
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