Learning Robust Deep Face Representation

17 Jul 2015  ·  Xiang Wu ·

With the development of convolution neural network, more and more researchers focus their attention on the advantage of CNN for face recognition task. In this paper, we propose a deep convolution network for learning a robust face representation... The deep convolution net is constructed by 4 convolution layers, 4 max pooling layers and 2 fully connected layers, which totally contains about 4M parameters. The Max-Feature-Map activation function is used instead of ReLU because the ReLU might lead to the loss of information due to the sparsity while the Max-Feature-Map can get the compact and discriminative feature vectors. The model is trained on CASIA-WebFace dataset and evaluated on LFW dataset. The result on LFW achieves 97.77% on unsupervised setting for single net. read more

PDF Abstract

Datasets


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods