Kinship classification based on discriminative facial patches

Recently there has been a large explosive growth of image data on social networks and how to use computer vision and machine learning technology to verify people relationships on these huge amount of human-centered image data remains a challenging issue. Remarkably, there have been few research attempts to analyze the possible human relationships on images, especially kin relationships. In this paper, we tackle a challenging and relatively new issue in kinship classification: determining the family that a query face image belongs to. To address this challenge, we propose a kinship classification method in three steps: (l)Discriminative patches are detected automatically in the facial landmark regions. (2) Appearance features, Histogram of Gradient (HOG), Scale-Invariant Feature Transform (SIFT) and Four-Patch Local Binary Pattern (FPLBP) are extracted from these patches respectively, and then we concatenate the features to create a high-dimensional feature vector. (3) Linear Support Vector Machine (SVM) with polynomial kernel is adopted to accomplish kinship classification task. Experimental evaluation results on Cornell Family 101 dataset demonstrate that our proposed method significantly outperforms the state-of-the-art kinship classification approaches.