Liu, Daqi, Bellotto, Nicola and Yue, Shigang (2020) Deep Spiking Neural Network for Video-based Disguise Face Recognition Based on Dynamic Facial Movements. IEEE Transactions on Neural Networks and Learning Systems, 31 (6). pp. 1843-1855. ISSN 2162-237X
Full content URL: https://ieeexplore.ieee.org/abstract/document/8767...
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Item Type: | Article |
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Item Status: | Live Archive |
Abstract
With the increasing popularity of social media andsmart devices, the face as one of the key biometrics becomesvital for person identification. Amongst those face recognitionalgorithms, video-based face recognition methods could make useof both temporal and spatial information just as humans do toachieve better classification performance. However, they cannotidentify individuals when certain key facial areas like eyes or noseare disguised by heavy makeup or rubber/digital masks. To thisend, we propose a novel deep spiking neural network architecturein this study. It takes dynamic facial movements, the facial musclechanges induced by speaking or other activities, as the sole input.An event-driven continuous spike-timing dependent plasticitylearning rule with adaptive thresholding is applied to train thesynaptic weights. The experiments on our proposed video-baseddisguise face database (MakeFace DB) demonstrate that theproposed learning method performs very well - it achieves from95% to 100% correct classification rates under various realisticexperimental scenarios
Keywords: | deep learning, neural network, face recognition, Spiking neural network(SNN), Machine learning |
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Subjects: | G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G760 Machine Learning G Mathematical and Computer Sciences > G730 Neural Computing G Mathematical and Computer Sciences > G740 Computer Vision |
Divisions: | College of Science > School of Computer Science |
ID Code: | 41718 |
Deposited On: | 05 Aug 2020 10:41 |
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