Abstract. In current face recognition approaches with convolutional
neural network (CNN), a pair of faces to compare are independently fed
into the CNN for feature extraction. For both faces the same kernels are
applied and hence the representation of a face stays fixed regardless of
whom it is compared with. As for us humans, however, one generally
focuses on varied characteristics of a face when comparing it with distinct persons as shown in Figure 1. Inspired, we propose a novel CNN
structure with what we referred to as contrastive convolution, which
specifically focuses on the distinct characteristics between the two faces
to compare, i.e., those contrastive characteristics. Extensive experiments
on the challenging LFW, and IJB-A show that our proposed contrastive
convolution significantly improves the vanilla CNN and achieves quite
promising performance in face verification task