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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Deep Spiking Neural Network for Video-Based Disguise Face Recognition Based on Dynamic Facial Movements
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Deep Spiking Neural Network for Video-Based Disguise Face Recognition Based on Dynamic Facial Movements

机译：基于动态面部运动的基于视频伪装人面识别的深度尖峰神经网络

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摘要

With the increasing popularity of social media and smart devices, the face as one of the key biometrics becomes vital for person identification. Among those face recognition algorithms, video-based face recognition methods could make use of both temporal and spatial information just as humans do to achieve better classification performance. However, they cannot identify individuals when certain key facial areas, such as eyes or nose, are disguised by heavy makeup or rubber/digital masks. To this end, we propose a novel deep spiking neural network architecture in this paper. It takes dynamic facial movements, the facial muscle changes induced by speaking or other activities, as the sole input. An event-driven continuous spike-timing-dependent plasticity learning rule with adaptive thresholding is applied to train the synaptic weights. The experiments on our proposed video-based disguise face database (MakeFace DB) demonstrate that the proposed learning method performs very well, i.e., it achieves from 95% to 100% correct classification rates under various realistic experimental scenarios.
机译：随着社交媒体和智能设备的普及越来越越来越多，脸部的一个关键生物识别性对人体识别至关重要。在那些面部识别算法中，基于视频的面部识别方法可以像人类那样使用时间和空间信息来实现更好的分类性能。然而，当某些关键面部区域（如眼睛或鼻子）伪装的橡胶/数字面具时，它们无法识别个人。为此，我们在本文中提出了一种新的深层尖峰神经网络架构。它采取动态面部运动，由于唯一的输入，通过说话或其他活动引起的面部肌肉变化。采用具有自适应阈值处理的事件驱动的连续峰值依赖性塑性学习规则以培训突触权重。关于我们所提出的基于视频的伪装面部数据库（MakeFace DB）的实验表明，所提出的学习方法非常好，即，它在各种现实实验场景下实现了95％至100％的正确分类率。

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