51. Deep Spiking Neural Network for Video-Based Disguise Face Recognition Based on Dynamic Facial Movements
- Author
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Daqi Liu, Nicola Bellotto, and Shigang Yue
- Subjects
G740 Computer Vision ,Biometrics ,Computer Networks and Communications ,Computer science ,Speech recognition ,Automated Facial Recognition ,Feature extraction ,Video Recording ,Action Potentials ,02 engineering and technology ,Facial recognition system ,Artificial Intelligence ,G730 Neural Computing ,Learning rule ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Humans ,Spiking neural network ,G400 Computer Science ,Thresholding ,Computer Science Applications ,Facial Expression ,Identification (information) ,Facial muscles ,medicine.anatomical_structure ,Face (geometry) ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,G760 Machine Learning ,Software ,Photic Stimulation - 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
- Published
- 2019
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