Abstract
The task of sketch face recognition refers to matching cross-modality facial images from sketch to photo, which is widely applied in the criminal investigation area. Existing works aim to bridge the cross-modality gap by inter-modality feature alignment approaches, however, the small sample problem has received much less attention, resulting in limited performance. In this paper, an effective Cross Task Modality Alignment Network (CTMAN) is proposed for sketch face recognition. To address the small sample problem, a meta learning training episode strategy is first introduced to mimic few-shot tasks. Based on the episode strategy, a two-stream network termed modality alignment embedding learning is used to capture more modality-specific and modality-sharable features, meanwhile, two cross task memory mechanisms are proposed to collect sufficient negative features to further improve the feature learning. Finally, a cross task modality alignment loss is proposed to capture modality-related information of cross task features for more effective training. Extensive experiments are conducted to validate the superiority of the CTMAN, which significantly outperforms state-of-the-art methods on the UoM-SGFSv2 set A, set B, CUFSF, and PRIP-VSGC dataset.