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

Deep face recognition: A survey

The face image is the most accessible biometric modality which is used for highly accurate face recognition systems, while it is vulnerable to many different types of presentation attacks. Face anti-spoofing is a very critical step before feeding the face image to biometric systems. In this paper, we propose a novel two-stream CNN-based approach for face anti-spoofing, by extracting the local features and holistic depth maps from the face images. The local features facilitate CNN to discriminate the spoof patches independent of the spatial face areas. On the other hand, holistic depth map examine whether the input image has a face-like depth. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, and Replay Attack), with comparison to the state of the art.

Face anti-spoofing using patch and depth-based CNNs

Automatic classification of environmental sounds, such as dog barking and glass breaking, is becoming increasingly interesting, especially for mobile devices. Most mobile devices contain both camer ...

Classifying environmental sounds using image recognition networks

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.

/pdf/deep-tree-learning-for-zero-shot-face-anti-spoofing-5cirhp7ro3.pdf

Deep Tree Learning for Zero-Shot Face Anti-Spoofing

Temporal features is important for face anti-spoofing Unfortunately existing methods have limitations to explore such temporal features In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations Our best model shows significant performance improvement over general CNN architecture (593% vs 734%), and hand-crafted features (593% vs 1000%) on CASIA dataset

Learning temporal features using LSTM-CNN architecture for face anti-spoofing

Video classification is more difficult than image classification since additional motion feature between image frames and amount of redundancy in videos should be taken into account. In this work, we proposed a new deep learning architecture called recurrent convolutional neural network (RCNN) which combines convolution operation and recurrent links for video classification tasks. Our architecture can extract the local and dense features from image frames as well as learning the temporal features between consecutive frames. We also explore the effectiveness of sequential sampling and random sampling when training our models, and find out that random sampling is necessary for video classification. The feature maps from our learned model preserve motion from image frames, which is analogous to the persistence of vision in human visual system. We achieved 81.0% classification accuracy without optical flow and 86.3% with optical flow on the UCF-101 dataset, both are competitive to the state-of-the-art methods.

Recurrent convolutional neural network for video classification

Facial landmark localization by enhanced convolutional neural network

This paper addresses the challenge of quickly reconstructing free-viewpoint videos of dynamic humans from sparse multi-view videos. Some recent works represent the dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets and reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric videos of dynamic humans from sparse view videos in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose a novel 2D motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than prior per-scene optimization methods while being competitive in the rendering quality. Training our model on a $512 \times 512$ video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code will be released on our project page: https://zju3dv.github.io/instant_nvr

Learning Neural Volumetric Representations of Dynamic Humans in Minutes

This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. Instead, we introduce a pose-driven deformation field based on the linear blend skinning algorithm, which combines the blend weight field and the 3D human skeleton to produce observation-to-canonical correspondences. Since 3D human skeletons are more observable, they can regularize the learning of the deformation field. Moreover, the pose-driven deformation field can be controlled by input skeletal motions to generate new deformation fields to animate the canonical human model. Experiments show that our approach significantly outperforms recent human modeling methods. The code is available at https://zju3dv.github.io/animatable_nerf/.

Zhenqi Xu

Papers

Learning temporal features using LSTM-CNN architecture for face anti-spoofing

Recurrent convolutional neural network for video classification

Facial landmark localization by enhanced convolutional neural network

Learning Neural Volumetric Representations of Dynamic Humans in Minutes

Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos