Abstract To solve the problem of kinship verification of facial image, an algorithm for neighborhood repulsed metric learning based on local feature fusion is proposed. Firstly, texture and skin color features are extracted from the key areas of the face images. Then, the feature fusion method is proposed. Finally, the metric learning method is introduced to learn a transformational matrix capable of making the distance between the samples with kinship smaller and the distance between the samples of non-kin larger. The prior knowledge of the similarity degree of existing data samples is utilized to learn the best similarity measure to describe the similarity of kinship samples better. The experimental results on KinFaceW-I and KinFaceW-II databases demonstrate the efficiency of the proposed algorithm.
Fund:Supported by National Natural Science Foundation of China(No.61071199), Natural Science Foundation of Hebei Province(No.F2016203422)
About author:: (HU Zhengping(Corresponding author), born in 1970, Ph.D., professor. His research interests include pattern recognition.) (GUO Zengjie, born in 1991, master student. His research interests include image processing and pattern recognition.) (WANG Meng, born in 1982, Ph.D. candidate. Her research interests include pattern recognition.) (SUN Zhe, born in 1990, Ph.D. candidate.Her research interests include sparse representation classification.)
HU Zhengping,GUO Zengjie,WANG Meng等. Neighborhood Repulsed Metric Learning for Kinship Verification Based on Local Feature Fusion[J]. , 2017, 30(6): 530-537.
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