Early and late features fusion for kinship verification based on constraint selection

Kinship verification is an interesting topic of face analysis. In this paper, we propose to extract several local image descriptors including Local Binary Pattern, Local Ternary Pattern and Histogram of Gradient for extracting features from facial images. Next, we apply an extension of constraint score for features ranking and then select a subset of feature for training by Support Vector Machine. The fusion at an early stage (feature level) and fusion at a later stage (score level) are compared and evaluated on two benchmark kinship datasets KinFaceW-I and KinFaceW-II. Experimental results show that the proposed approach outperforms other works in the state-of-the-art.