Semi-Coupled Synthesis and Analysis Dictionary Pair Learning for Kinship Verification

Xiaopan Chen, Xiaoke Zhu, Shanshan Zheng, Taihao Zheng, Fan Zhang

Abstract: Kinship verification is an interesting and important problem in the fields of computer vision. In practice, the biggest obstacle in kinship verification is that the representation capability of extracted features may not be powerful due to the significant differences between facial images of family members. To effectively address this problem, we propose a semi-coupled synthesis and analysis dictionary pair learning (SSADL) approach, which can reduce the differences between facial images. Specifically, SSADL jointly learns two view-specific synthesis-analysis dictionary pairs as well as a mapping matrix from the training data of parent and child, with which, the heterogeneous facial images of parent and child can be transformed into coding coefficients of the same subspace, such that the kinship verification task can be conducted using the coding coefficients. Besides, we also design a hard sample based coefficient discriminant term to ensure that the obtained coefficients own favorable discriminability. Experimental results on several publicly used benchmarks show the effectiveness of our proposed approach.