Distance and Direction based Deep Discriminant Metric Learning for Kinship Verification

Image-based kinship verification is an important task in computer vision and has many applications in practice, such as missing children search, family album construction, etc. Due to the differences in age, gender, expression and appearance, there usually exists large discrepancy between the facial images of parent and child. This makes kinship verification a challenging task. In this paper, we propose a Distance and Direction based Deep Discriminant Metric Learning (D4ML) approach for kinship verification. The basic idea of D4ML is to make full use of the discriminant information contained in the facial images of parent and child, such that the network can learn more discriminating distance metric. Specifically, D4ML learns metric by utilizing the discriminant information from two perspectives: distance-based perspective and direction-based perspective. From the distance-based perspective, the designed loss function is used to minimize the distance between images having kinship and maximize the distance between images without kinship. In practice, the gender difference and large age gap may significantly increase the distance between facial images of parent and child. Therefore, learning metric only from distance-based perspective is insufficient. Considering that two vectors with large distance may appear high similarity in direction, D4ML also employs the direction-based loss function in the training process. Both kinds of loss function work together to improve the discriminability of the learned metric. Experimental results on four small size publicly available datasets demonstrate the effectiveness of our approach. Source code of our approach can be found in https://github.com/lclhenu/D4ML.