Sparse discriminative multi-manifold embedding for one-sample face identification

作者：

Highlights：

• A one-sample face identification scheme is proposed based on sparse discriminative multi-manifold embedding;

• Based on structured sparse graphs, a novel manifold embedding technique is proposed for discriminative feature learning;

• A global manifold distance is introduced for recognition;

• Much better results have been achieved than current methods.

摘要

•A one-sample face identification scheme is proposed based on sparse discriminative multi-manifold embedding;•Based on structured sparse graphs, a novel manifold embedding technique is proposed for discriminative feature learning;•A global manifold distance is introduced for recognition;•Much better results have been achieved than current methods.

论文关键词：Face recognition,Structured sparse representation,Manifold embedding

论文评审过程：Received 19 January 2014, Revised 14 January 2015, Accepted 21 September 2015, Available online 2 October 2015, Version of Record 24 December 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.09.024