Max-Feature-Map Based Light Convolutional Embedding Networks for Face Verification

Abstract

The powerful image feature extraction ability of convolutional neural network makes it possible to achieve great success in the field of face recognition. However, this category of models tend to be deep and paralleled which is not capable to be applied in real-time face recognition tasks. In order to improve its feasibility, we propose a max-feature-map activation based fully convolutional structure to extract face features with higher speed and less computational cost. The learned model has a great potential on embedding in the hardware devices due to its high recognition performance and small storage space. Experimental results demonstrate that the proposed model is 63 times smaller in comparison with the famous VGG model. At the same time, 96.80% verification accuracy is achieved for a single network on LFW benchmark.