基于改进深度残差网络的低功耗表情识别

doi:10.11896／j.issn.1002-137X.2018.09.051

Abstract

Abstract: To improve recognition rate and reduce power consumption of facial expression recognition systems,this paper proposed a facial expression recognition method using an improved deep residual networks(ResNets).Residual learning solves the degradation problem of the deep Convolutional Neural Networks(CNNs) to a certain degree and increases the network layers infinitely,but it makes deep CNNs face a more serious power consumption problem.To solve this problem,this paper introduced a new biologically-plausible activation function to improve ResNets and get a facial expression recognition method with both higher performance and lower power consumption.The Rectified Linear Units(ReLU) in the convolutional layers of ResNets are replaced with the new activation function Noisy Softplus.The obtained weights by using the improved ResNets can be directly applied to a deep Spiking Neural Networks(SNNs) architecture derived from the ResNets.The experimental results suggest that the proposed facial expression recognition method is able to achieve higher recognition rate and lower power consumption on a neuromorphic hardware.

Key words: Convolutional neural networks, Facial expression recognition, Leaky Integrate and Fire(LIF) neurons, Residual networks

CLC Number:

TP183

DU Jin, CHEN Yun-hua, ZHANG Ling, MAI Ying-chao. Energy-efficient Facial Expression Recognition Based on Improved Deep Residual Networks[J].Computer Science, 2018, 45(9): 303-307.

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Energy-efficient Facial Expression Recognition Based on Improved Deep Residual Networks

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