Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

Gene loss and co-option of toll-like receptors facilitate paternal immunological adaptation in the brood pouch of pregnant male seahorses

Male pregnancy in syngnathids (seahorses, pipefishes, and sea dragons) is an evolutionary innovation in the animal kingdom. Paternal immune resistance to the fetus is a critical challenge, particularly in seahorses with fully enclosed brood pouches and sophisticated placentas. In this study, comparative genomic analysis revealed that all syngnathid species lost three vertebrate-conserved Toll-like receptors (TLR1, TLR2, and TLR9), of which all play essential roles in immune protection and immune tolerance in the uterus and placenta. Quantitative real-time PCR (qRT-PCR) analysis showed that the TLR paralog genes including TLR18, TLR25, and TLR21 were highly expressed in the placenta inside the seahorse brood pouch and changed dynamically during the breeding cycle, suggesting the potentially important role of the TLRs during male pregnancy. Furthermore, the immune challenge test in vitro showed a remarkable expression response from all three TLR genes to specific pathogenic antigens, confirming their immune function in seahorse brood pouches. Notably, the altered antigen recognition spectrum of these genes appeared to functionally compensate in part for the lost TLRs, in contrast to that observed in other species. Therefore, we suggest that gene loss and co-option of TLRs may be a typical evolutionary strategy for facilitating paternal immunological adaptation during male pregnancy