scholarly journals Organizing Multimedia Data in Video Surveillance Systems Based on Face Verification with Convolutional Neural Networks

2017 ◽  
pp. 223-230 ◽  
Author(s):  
Anastasiia D. Sokolova ◽  
Angelina S. Kharchevnikova ◽  
Andrey V. Savchenko
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Video Surveillance ◽  
Multimedia Data ◽  
Face Verification ◽  
Surveillance Systems

scholarly journals Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks

2017 ◽  
Vol 126 (2-4) ◽  
pp. 272-291 ◽  
Author(s):  
Jun-Cheng Chen ◽  
Rajeev Ranjan ◽  
Swami Sankaranarayanan ◽  
Amit Kumar ◽  
Ching-Hui Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Face Verification ◽  
Deep Convolutional Neural Networks

scholarly journals Classifying Behaviours in Videos with Recurrent Neural Networks

2017 ◽  
Vol 7 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Javier Abellan-Abenza ◽  
Alberto Garcia-Garcia ◽  
Sergiu Oprea ◽  
David Ivorra-Piqueres ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Surveillance ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Video Images ◽  
Employee Monitoring

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.

scholarly journals Learning face similarities for face verification using hybrid convolutional neural networks

2019 ◽  
Vol 16 (3) ◽  
pp. 1333
Author(s):  
Fadhlan Hafizhelmi Kamaru Zaman ◽  
Juliana Johari ◽  
Ahmad Ihsan Mohd Yassin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Face Verification ◽  
Test Accuracy ◽  
Test Protocol ◽  
Face Pair ◽  
Face Images ◽  
The Face ◽  
In The Wild

<span>Face verification focuses on the task of determining whether two face images belong to the same identity or not. For unrestricted faces in the wild, this is a very challenging task. Besides significant degradation due to images that have large variations in pose, illumination, expression, aging, and occlusions, it also suffers from large-scale ever-expanding data needed to perform one-to-many recognition task. In this paper, we propose a face verification method by learning face similarities using a Convolutional Neural Networks (ConvNet). Instead of extracting features from each face image separately, our ConvNet model jointly extracts relational visual features from two face images in comparison. We train four hybrid ConvNet models to learn how to distinguish similarities between the face pair of four different face portions and join them at top-layer classifier level. We use binary-class classifier at top-layer level to identify the similarity of face pairs which includes a conventional Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), Native Bayes, and another ConvNet. There are 3 face pairing configurations discussed in this paper. Results from experiments using Labeled face in the Wild (LFW) and CelebA datasets indicate that our hybrid ConvNet increases the face verification accuracy by as much as 27% when compared to individual ConvNet approach. We also found that Lateral face pair configuration yields the best LFW test accuracy on a very strict test protocol without any face alignment using MLP as top-layer classifier at 87.89%, which on-par with the state-of-the-arts. We showed that our approach is more flexible in terms of inferencing the learned models on out-of-sample data by testing LFW and CelebA on either model.</span>

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