Distance metric optimization driven convolutional neural network for age invariant face recognition

作者：

Highlights：

• Propose a new distance metric optimization driven deep-learning framework for age-invariant face recognition.

• Learn feature representations and the similarity measure simultaneously in an end-to-end way.

• Train the joint network using a novel optimization method and carefully designed training strategies.

• The experimental results demonstrate the effectiveness of our approach.

摘要

•Propose a new distance metric optimization driven deep-learning framework for age-invariant face recognition.•Learn feature representations and the similarity measure simultaneously in an end-to-end way.•Train the joint network using a novel optimization method and carefully designed training strategies.•The experimental results demonstrate the effectiveness of our approach.

论文关键词：Age invariant,Face recognition,Distance metric,Deep CNN,Joint learning

论文评审过程：Received 14 November 2016, Revised 30 September 2017, Accepted 7 October 2017, Available online 17 October 2017, Version of Record 21 November 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.015