Face verification based on deep Bayesian convolutional neural network in unconstrained environment

Abstract

Unconstrained face verification aims to verify whether two specify images contain the same person. In this paper, we propose a deep Bayesian convolutional neural network (DBCNN) framework to extract facial features and measure their similarity for face verification in unconstrained conditions. Specifically, we design a deep convolutional neural network and construct a Bayesian probabilistic model by transferring the Bayesian likelihood ratio function into linear decision function. By training a decision line rather than finding a suitable threshold, we further enlarge the distances between inter-class and intra-class in unconstrained environment. Finally, we comprehensively evaluate our method on LFW, CACD-VS and MegaFace datasets. The test results on LFW and CACD-VS datasets show that our method can shrink intra-class variations significantly. The performance of our DBCNN model on MegaFace dataset proves that our model can achieve comparable performance to state-of-the-art methods on face verification with relative small training data and only one single network.