ACLMHA and FML: A brain-inspired kinship verification framework

Abstract

As an extended research direction of face recognition, kinship verification based on the face image is an interesting yet challenging task, which aims to determine whether two individuals are kin-related based on their facial images. Face image-based kinship verification benefits many applications in real life, including: missing children search, family photo classification, kinship information mining, family privacy protection, etc. Studies presented thus far provide evidence that face kinship verification still offers many challenges. Hence in this paper, we propose a novel kinship verification architecture, the main contributions of which are as follows: To boost the deep model to capture various and abundant local features from different local face regions, we propose an attention center learning guided multi-head attention mechanism to supervise the learning of attention weights and make different attention heads notice the characteristics of different regions. To combat the misclassification caused by single feature center loss, we propose a family-level multi-center loss to ensure a more proper intra/inter-class distance measurement for kinship verification. To measure the potential similarity of features among relatives better, we propose to introduce the relation comparison module to measure the similarity among features at a deeper level. Extensive experiments are conducted on the widely used kinship verification dataset—Family in the Wild (FIW) dataset. Compared with other state-of-art (SOTA) methods, encouraging results are obtained, which verify the effectiveness of our proposed method.