Abstract: In order to solve the problem that the accuracy rate may decrease and the memory occupancy rate may still be high when the convolution neural network with fused depth hash is used for face recognition,this paper proposed a deep face recognition algorithm based on weighted hashing.Firstly,a fully convolutional neural network of deep hash based on dimension splicing with high and low dimensional features is proposed to improve recognition accuracy.Secondly,a model compression method with floating-point weights quantized into hash coding is proposed to reduce memory occupancy rate of the model.The experimental results show that the proposed method improves efficiency by 68%,improves the Rank-1 accuracy by 1.67%,and the model size is compressed by 91.2% when it is improved based on VGG framework.In addition,it improves efficiency by 61% when the Rank-1 accuracy is slightly improved,and the model size is reduced by 42.24% when it is improved based on Sphereface framework.The results indicate that the proposed method can improve the recognition accuracy and efficiency,and reduce the memory usage.It also can be applied for other frameworks.

Key words: Deep hash, Dimension splicing, Full convolution network (FCN), Model compression