Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues. A CNN-RNN model is learned to estimate the face depth with pixel-wise supervision, and to estimate rPPG signals with sequence-wise supervision. The estimated depth and rPPG are fused to distinguish live vs. spoof faces. Further, we introduce a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations. Experiments show that our model achieves the state-of-the-art results on both intra- and cross-database testing.

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Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

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Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

DistancePPG: Robust non-contact vital signs monitoring using a camera

Face anti-spoofing is designed to keep face recognition systems from recognizing fake faces as the genuine users. While advanced face anti-spoofing methods are developed, new types of spoof attacks are also being created and becoming a threat to all existing systems. We define the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous works of ZSFA only study 1-2 types of spoof attacks, such as print/replay attacks, which limits the insight of this problem. In this work, we expand the ZSFA problem to a wide range of 13 types of spoof attacks, including print attack, replay attack, 3D mask attacks, and so on. A novel Deep Tree Network (DTN) is proposed to tackle the ZSFA. The tree is learned to partition the spoof samples into semantic sub-groups in an unsupervised fashion. When a data sample arrives, being know or unknown attacks, DTN routes it to the most similar spoof cluster, and make the binary decision. In addition, to enable the study of ZSFA, we introduce the first face anti-spoofing database that contains diverse types of spoof attacks. Experiments show that our proposed method achieves the state of the art on multiple testing protocols of ZSFA.

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Deep Tree Learning for Zero-Shot Face Anti-Spoofing

Existing domain generalization methods for face anti-spoofing endeavor to extract common differentiation features to improve the generalization. However, due to large distribution discrepancies among fake faces of different domains, it is difficult to seek a compact and generalized feature space for the fake faces. In this work, we propose an end-to-end single-side domain generalization framework (SSDG) to improve the generalization ability of face anti-spoofing. The main idea is to learn a generalized feature space, where the feature distribution of the real faces is compact while that of the fake ones is dispersed among domains but compact within each domain. Specifically, a feature generator is trained to make only the real faces from different domains undistinguishable, but not for the fake ones, thus forming a single-side adversarial learning. Moreover, an asymmetric triplet loss is designed to constrain the fake faces of different domains separated while the real ones aggregated. The above two points are integrated into a unified framework in an end-to-end training manner, resulting in a more generalized class boundary, especially good for samples from novel domains. Feature and weight normalization is incorporated to further improve the generalization ability. Extensive experiments show that our proposed approach is effective and outperforms the state-of-the-art methods on four public databases. The code is released online.

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Single-Side Domain Generalization for Face Anti-Spoofing

The present invention relates to 3D mask face anti-spoofing with remote photoplethysmography. More particularly, it relates to a local rPPG correlation model to extract discriminative and robust local rPPG signal patterns so that an imposter can better be detected regardless of the material and quality of the mask.

/pdf/3d-mask-face-anti-spoofing-with-remote-photoplethysmography-20zhbjxvym.pdf

3D mask face anti-spoofing with remote photoplethysmography

3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention. Recently, remote Photoplethysmography (rPPG) is employed as an intrinsic liveness cue which is independent of the mask appearance. Although existing rPPG-based methods achieve promising results on both intra and cross dataset scenarios, they may not be robust enough when rPPG signals are contaminated by noise. In this paper, we propose a new liveness feature, called rPPG correspondence feature (CFrPPG) to precisely identify the heartbeat vestige from the observed noisy rPPG signals. To further overcome the global interferences, we propose a novel learning strategy which incorporates the global noise within the CFrPPG feature. Extensive experiments indicate that the proposed feature not only outperforms the state-of-the-art rPPG based methods on 3D mask attacks but also be able to handle the practical scenarios with dim light and camera motion.

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Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection

3D Mask face spoofing attack becomes new challenge and attracts more research interests in recent years. However, due to the deficiency number and limited variations of database, there are few methods be proposed to aim on it. Meanwhile, most of existing databases only concentrate on the anti-spoofing of different kinds of attacks and ignore the environmental changes in real world applications. In this paper, we build a new 3D mask anti-spoofing database with more variations to simulate the real world scenario. The proposed database contains 12 masks from two companies with different appearance quality. 7 Cameras from the stationary and mobile devices and 6 lighting settings that cover typical illumination conditions are also included. Therefore, each subject contains 42 (7 cameras * 6 lightings) genuine and 42 mask sequences and the total size is 1008 videos. Through the benchmark experiments, directions of the future study are pointed out. We plan to release the database as an platform to evaluate methods under different variations.

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A 3D Mask Face Anti-Spoofing Database with Real World Variations

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.

/pdf/data-gru-dual-attention-time-aware-gated-recurrent-unit-for-4aafjshpqi.pdf

DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

Remote PPG (rPPG) has been attracting increasing attention due to its potential in a wide range of application scenarios such as clinical monitoring, physical training and face presentation attack detection. On top of manually designed solutions, the deep-learning approach appears in rPPG estimation and achieves top-level performance. However, most of them try to integrate the steps of both preprocessing and ROI selection into an end-to-end network, which limits the generalization in other applications that use different input skin regions. The ROI selection model learned on face videos may not adapt to skin regions from other body parts with different appearance and size. In this paper, we leave the preprocessing apart and propose a lightweight rPPG estimation network—DeeprPPG for general use. DeeprPPG is based on spatiotemporal convolutions and can be used as a well-defined module in wider application scenarios with different types of input skin. To further boost the robustness, a spatiotemporal rPPG aggregation strategy is designed to adaptively aggregate rPPG signals from multiple skin regions into the final one. Extensive experiments are conducted and the results illustrate its robustness when facing unseen skin regions and unseen scenarios.

Siqi Liu

Papers

3D mask face anti-spoofing with remote photoplethysmography

Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection

A 3D Mask Face Anti-Spoofing Database with Real World Variations

DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

A General Remote Photoplethysmography Estimator with Spatiotemporal Convolutional Network