Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models

作者：

Highlights：

• A local matching Gabor method is improved with an Active Shape and a Statistical Model.

• The enhanced model is applied to recognize faces with significant pose variation.

• Comprehensive tests were performed on the FERET and CMU-PIE databases.

• Results improved from 31.1% to 70.57% in the extreme poses of the databases.

• We reached the highest results with pose variation compared to any previous 2D method.

摘要

Highlights•A local matching Gabor method is improved with an Active Shape and a Statistical Model.•The enhanced model is applied to recognize faces with significant pose variation.•Comprehensive tests were performed on the FERET and CMU-PIE databases.•Results improved from 31.1% to 70.57% in the extreme poses of the databases.•We reached the highest results with pose variation compared to any previous 2D method.

论文关键词：Face recognition across pose,Statistical model for face recognition,Active shape model,Gabor features,Entropy weighting

论文评审过程：Received 30 November 2014, Revised 25 April 2015, Accepted 19 May 2015, Available online 29 May 2015, Version of Record 16 July 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.05.017