%0 Journal Article
%A LI Ya
%A WANG Guang-run
%A WANG Qing
%T A Deep Joint Learning Approach for Age Invariant Face Verification
%D 2017
%R 10.13190/j.jbupt.2017.01.015
%J Journal of Beijing University of Posts and Telecommunications
%P 84-88,110
%V 40
%N 1
%X A joint learning approach (JLA) based on deep convolutional neural network (CNN) for age-invariant face verification was proposed. Feature representation, distance metric and decision function can be learned simultaneously thereafter. Comparing with traditional approaches, it uses fix threshold, so the match errors caused by unfit threshold can be avoided. Some strategies to overcome insufficient memory capacity, prevent over-fitting and reduce computational cost were also introduced. Experiment demonstrates the effectiveness of this approach; the rank-1 recognition accuracy is improved to 93.6% on the MORPH-II database.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017.01.015