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

End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture are stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generative-discriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-art methods in both photo-sketch generation and face sketch verification.Comment: 8 pages, 6 figures. Proceeding in ACM International Conference on Multimedia Retrieval (ICMR), 201

Super-concentrated alkali hydroxide electrolytes for rechargeable Zn batteries

Rechargeable Zn batteries offer safe, inexpensive energy storage, but when deeply discharged to compete with lithium-ion batteries, they are plagued by parasitic reactions at the Zn anodes. We apply super-concentrated alkaline electrolytes to suppress two key parasitic reactions, hydrogen evolution and ZnO passivation. An electrolyte with 15 M KOH displays a broad electrochemical window (>2.5 V on Au), a high ZnO solubility (>1.5 M), and an exceptionally high ionic conductivity (>0.27 S/cm at 25 C). Spectroscopies and ab-initio molecular dynamics simulation suggest K+-OH- pairs and a tightened water network to underpin the stability. The simulation further reveals unique triggered proton hopping that offsets the lack of water wires to sustain the conductivity. Low hydrogen evolution, confirmed via online mass spectroscopy, and slow passivation enable a NiOOH||Zn battery to deliver a cumulative capacity of 8.4 Ah cm-2 and a Zn-air battery to last for over 110 hours

SARS-associated Coronavirus Transmitted from Human to Pig

Severe acute respiratory syndrome–associatedcoronavirus (SARS-CoV) was isolated from a pig during a survey for possible routes of viral transmission after a SARS epidemic. Sequence and epidemiology analyses suggested that the pig was infected by a SARS-CoV of human origin