A deep joint learning approach for age invariant face verification

Abstract

Age-related research has become an attractive topic in recent years due to its wide range of application scenarios. In spite of the great advancement in face related works in recent years, face recognition across ages is still a challenging problem. In this paper, we propose a new deep Convolutional Neural Network (CNN) model for age-invariant face verification, which can learn features, distance metrics and threshold simultaneously. We also introduce two tricks to overcome insufficient memory capacity issue and to reduce computational cost. Experimental results show our method outperforms other state-of-the-art methods on MORPH-II database, which improves the rank-1 recognition rate from the current best performance 92.80% to 93.6%.