Fully automatic face normalization and single sample face recognition in unconstrained environments
作者：
Highlights：
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• We present a fully automatic face normalization and recognition system.
• It normalizes the face images for both in-plane and out-of-plane pose variations.
• The performance of AAM fitting is improved using a novel initialization technique.
• HOG and Gabor features are fused using CCA to have more discriminative features.
• The proposed system recognizes non-frontal faces using only a single gallery sample.
摘要
•We present a fully automatic face normalization and recognition system.•It normalizes the face images for both in-plane and out-of-plane pose variations.•The performance of AAM fitting is improved using a novel initialization technique.•HOG and Gabor features are fused using CCA to have more discriminative features.•The proposed system recognizes non-frontal faces using only a single gallery sample.
论文关键词：Face recognition in-the-wild,Pose-invariance,Frontal face synthesizing,Feature-level fusion,Canonical correlation analysis,Active appearance models
论文评审过程：Received 12 July 2015, Revised 29 October 2015, Accepted 31 October 2015, Available online 19 November 2015, Version of Record 30 November 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.10.047
