A novel algorithm using affine-invariant features for pose-variant face recognition

作者：Zhao Youen^*; Li Li; Liu Zhaoguang

来源：Computers & Electrical Engineering, 2015, 46: 217-230.

DOI：10.1016/j.compeleceng.2014.12.010

• Face recognition
• Pose variance
• Affine-invariant feature
• Multi-scale autoconvolution
• Principal component analysis
• Illumination change

Pose variation is the major factor significantly affecting recognition efficiency in the field of face recognition. In this paper, we propose a novel algorithm for pose-variant face recognition. We first use the affine-invariant multi-scale autoconvolution (MSA) transformation to extract pose-invariant features. Following this, we use principal component analysis (PCA) to decorrelate the feature sets and reduce the number of required MSA dimensions. The PCA components with sufficiently large corresponding eigenvalues are then passed to the k nearest classifier. Compared with other pose-variant face recognition techniques, our proposed algorithm exhibits a superior ability to pose changes, illumination changes, and different face databases, moreover records a higher recognition rate.

• 出版日期2015-8
• 单位山东财经大学