Deep Learning models for Video based Facial Recognition Systems: A Survey
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2018 by IJCTT Journal | ||
| Volume-60 Number-3 |
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| Year of Publication : 2018 | ||
| Authors : K.Sunitha | ||
| DOI : 10.14445/22312803/IJCTT-V60P122 |
K.Sunitha "Deep Learning models for Video based Facial Recognition Systems: A Survey". International Journal of Computer Trends and Technology (IJCTT) V60(3):144-150 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract
Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. Face recognition (FR) systems for video surveillance (VS) applications attempt to accurately detect the presence of target individuals over a distributed network of cameras. Specifically, in still-to-video FR application, a single high-quality reference still image captured with still camera under controlled conditions is employed to generate a facial model to be matched later against lower-quality faces captured with video cameras under uncontrolled conditions. Current video-based FR systems can perform well on controlled scenarios, while their performance is not satisfactory in uncontrolled scenarios mainly because of the differences between the source (enrollment) and the target (operational) domains. Most of the efforts in this area have been toward the design of robust video-based FR systems in unconstrained surveillance environments. deep learning architectures proposed in the literature based on triplet-loss function (e.g., cross-correlation matching CNN, trunk-branch ensemble CNN and HaarNet) and supervised autoencoders (e.g., canonical face representation CNN) are studied.
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Keywords
Deep Learning, Face Recognition, Video Surveillance, CNN.