EffectFace:A Fast and Efficient Deep Neural Network Model for Face Recognition

摘要：

　　Despite the Deep Neural Network(DNN)has achieved a great success in image recognition,the resource needed by DNN applications is still too much in terms of both memory usage and computing time,which makes it barely possible to deploy a whole DNN system on resource-limited devices such as smartphones and small embedded systems.In this paper,we present a DNN model named EffectFace designed for higher storage and computation efficiency without compromising the accuracy.EffectFace includes two sub-modules,EffectDet for face detection and EffectApp for face recognition.In EffectDet we use sparse and small-scale convolution cores(filters)to reduce the number of weights for less memory usage.In EffectApp,we use pruning and weights-sharing technology to further reduce weights.At the output stage of the network,we use a new loss function rather than the traditional Softmax function to acquire feature vectors of the input face images,which reduces the dimension of the output of the network from n to fixed 128 where n equals to the number of categories to classify.Experiments show that,compared with previous models,the amounts of weights of our EffectFace is dramatically decreased(less than 10%of previous models)without losing the accuracy of recognition.

关键词： Deep learning Efficient neural network Face recognition

作者: Weicheng Li Dan Jia Jia Zhai Jihong Cai Han Zhang Lianyi Zhang Hailong Yang Depei Qian Rui Wang

作者单位: School of Computer Science and Engineering,Beihang University,Beijing,China Communication University of China,Beijing,China;Science and Technology on Electromagnetic Scattering Science and Technology on Special System Simulation Laboratory,Beijing Simulation Center,Beijing,Chi

会议类型: 国际会议

会议名称: the 12th Conference on Advanced Computer Architecture?(ACA 2018)(2018年全国计算机体系结构学术年会)

会议地点: 辽宁营口

会议语种:英文

页码: 127-139

在线出版日期: 2018-08-10（万方平台首次上网日期，不代表论文的发表时间）