关键词: 亲属关系认证, 测度学习, 稀疏表示, 相似性度量

Abstract: A new algorithm based on neighborhood repulsed metric learning sparse discriminant for single sample is proposed to solve the problem of kinship verification with facial image. Firstly, a new distance metric is learnt under which facial images with kinship relations are projected as close as possible and those without kinship relations in the neighborhoods are pulled as far as possible. The purpose is to use the similarity of existing data samples to learn a distance metric which can better describe the similarity of samples. Then, the sparse representation method is adopted to establish a dictionary which can be used to linearly represent the children images under the new distance metric space, and the sparse coefficient is applied to measure the similarity of different samples. In addition, to deal with the inconspicuous similarity of kinship samples, a novel algorithm based on sub-modular sparse discriminant is proposed. Finally, the multiple sparse coefficients are used to decide whether there is a kinship relation between the two input samples. The experiment results on the KinFaceW-I database and KinFaceW-II dataset demonstrate the efficiency of the proposed method.

Key words: kinship verification, metric learning, sparse representation, similarity metric