Chuanxin Lan’s scientific contributions

Face presentation attack detection (PAD) has drawn increasing attentions to secure face recognition (FR) systems which are being widely used in many applications from access control to smartphone unlock. Traditional approaches for PAD may lack good generalization capability into new application scenarios due to the limited number of subjects and data modality. In this work, we propose an end-to-end multi-modal fusion approach via spatial and channel attention to improve PAD performance on CASIA-SURF. Specifically , we first build four branches integrated with spatial and channel attention module to obtain the uniform features of different modalities, i.e., RGB, Depth, IR and the fused modality with 9 channels which concatenating three modalities. Subsequently, the features extracted from the four branches are concatenated and fed into the shared layers to learn more discriminative features from the fusion perspective. Finally, we get the classification confidence scores w.r.t. PAD or not. The entire network is optimized with the joint of the center loss and softmax loss and SGRD solver to update the parameters. The proposed approach shows promising results on the CASIA-SURF dataset.

While remarkable progress has been made to pedestrian detection in recent years, robust pedestrian detection in the wild e.g., under surveillance scenarios with occlusions, remains a challenging problem. In this paper, we present a novel approach for joint pedestrian and body part detection via semantic relationship learning under unconstrained scenarios. Specifically, we propose a Body Part Indexed Feature (BPIF) representation to encode the semantic relationship between individual body parts (i.e., head, head-shoulder, upper body, and whole body) and highlight per body part features, providing robustness against partial occlusions to the whole body. We also propose an Adaptive Joint Non-Maximum Suppression (AJ-NMS) to replace the original NMS algorithm widely used in object detection, leading to higher precision and recall for detecting overlapped pedestrians. Experimental results on the public-domain CUHK-SYSU Person Search Dataset show that the proposed approach outperforms the state-of-the-art methods for joint pedestrian and body part detection in the wild.