针对如何利用人脸图像局部特征进行亲属关系认证的问题,文中提出基于局部特征融合的邻域排斥度量学习亲属关系认证算法.首先抽取脸部的关键区域,分别对每块关键区域提取纹理和肤色特征.然后进行特征融合.最后引入度量学习,学习能使具有亲属关系样本距离变小、非亲属关系样本距离变大的变换矩阵,利用已有数据样本间相似程度的先验知识学习最佳相似性度量,更好地刻画亲属样本间的相似关系.在KinFaceW-I和KinFaceW-II数据库中的实验表明,相比已有的亲属关系认证算法,文中算法性能更好.%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.
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