Despite the development of spectral sensors and spectral data-driven learning methods which have led to significant advances in face anti-spoofing (FAS), the singular dimensionality of spectral information often results in poor robustness and weak generalization. Polarization, another fundamental property of light, can reveal intrinsic differences between genuine and fake faces with advantaged performance in precision, robustness, and generalizability. In this paper, we propose a facial image translation method from visible light (VIS) to polarization (VPT), capable of generating valuable polarimetric optical characteristics for facial presentation attack detection using VIS spectrum information input only. Specifically, the VPT method adopts a multi-stream network structure, comprising a main network and two branch networks, to translate VIS images into degree of polarization (DoP) images and Stokes polarization parameters${S}_{1}$and${S}_{2}$. To further improve image translation quality, we introduce a frequency-domain consistency loss as a complement to the existing spatial losses to narrow the gap in the frequency domain. The physical mapping relations for the DoP and Stokes parameters are employed, and the Stokes loss is designed to ensure that the generated polarization modalities conform to objective physical laws. Extensive experiments on the CASIA-Polar and CASIA-SURF datasets demonstrate the superiority of VPT over other baseline methods in terms of polarization image quality and its remarkable performance in the FAS task. This work leverages the inherent physical advantages of polarization information in material discrimination tasks while addressing hardware limitations in polarization image collection, proposing a novel solution for face recognition system security control.

In face antispoofing, it is desirable to have multimodal images to demonstrate liveness cues from various perspectives. However, in most face recognition scenarios, only a single modality, namely visible lighting (VIS) facial images is available. This paper first investigates the possibility of generating polarized (Polar) images from VIS cameras without changing the existing recognition devices to improve the accuracy and robustness of Presentation Attack Detection (PAD) in face biometrics. A novel multimodal face antispoofing framework is proposed based on the machine-learning relationship between VIS and Polar images of genuine faces. Specifically, a dual-modal central differential convolutional network (CDCN) is developed to capture the inherent spoofing features between the VIS and the generated Polar modalities. Quantitative and qualitative experimental results show that our proposed framework not only generates realistic Polar face images but also improves the state-of-the-art face anti-spoofing results on the VIS modal database (i.e. CASIA-SURF). Moreover, a polar face database, CASIA-Polar, has been constructed and will be shared with the public at http://biometrics.idealtest.org to inspire future applications within the biometric anti-spoofing field.

Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accurate detection of structural variants is a challenging task. Especially existing structural variant detection approaches produce a large proportion of incorrect calls, so effective structural variant filtering approaches are urgently needed. In this study, we propose a novel deep learning-based approach, DeepSVFilter, for filtering structural variants in short read whole genome sequencing data. DeepSVFilter encodes structural variant signals in the read alignments as images and adopts the transfer learning with pre-trained convolutional neural networks as the classification models, which are trained on the well-characterized samples with known high confidence structural variants. We use two well-characterized samples to demonstrate DeepSVFilter's performance and its filtering effect coupled with commonly used structural variant detection approaches. The software DeepSVFilter is implemented using Python and freely available from the website at https://github.com/yongzhuang/DeepSVFilter.