Many face anti-spoofing (FAS) methods have focused on learning discriminative features from both live and spoof training data to strengthen the security of face recognition systems. However, since not every possible attack type is available in the training stage, these FAS methods usually fail to detect unseen attacks in the inference stage. In comparison, one-class FAS, where training data comprise only live faces, aims to detect whether a test face image belongs to the live class or not. In this paper, we propose a novel One-Class Spoof Cue Map estimation Network (OC-SCMNet) to address the one-class FAS detection problem. Our first goal is to learn to extract latent spoof features from live images so that their estimated Spoof Cue Maps (SCMs) should have zero responses. To avoid trapping to a trivial solution, we devise a novel SCM-guided feature learning by combining many SCMs as pseudo ground-truths to guide a conditional generator to create latent spoof features for spoof data. Our second goal is to simulate the potential out-of-distribution spoof attacks approximately. To this end, we propose using a memory bank to dynamically preserve a set of sufficiently “independent” latent spoof features to encourage the generator to probe the latent spoof feature space. Extensive experiments conducted on eight FAS benchmark datasets demonstrate that the proposed OC-SCMNet not only outperforms previous one-class FAS approaches but also achieves performance comparable to the state-of-the-art two-class FAS methods. The code is available at https://github.com/Pei-KaiHuang/CVPR24_OC_SCMNet.

Face anti-spoofing (FAS) plays a pivotal role in ensuring the security and reliability of face recognition systems. With advancements in vision-language pretrained (VLP) models, recent two-class FAS techniques have leveraged the advantages of using VLP guidance, while this potential remains unexplored in one-class FAS methods. The one-class FAS focuses on learning intrinsic liveness features solely from live training images to differentiate between live and spoof faces. However, the lack of spoof training data can lead one-class FAS models to inadvertently incorporate domain information irrelevant to the live/spoof distinction (\eg, facial content), causing performance degradation when tested with a new application domain. To address this issue, we propose a novel framework called Spoof-aware one-class face anti-spoofing with Language Image Pretraining (SLIP). Given that live faces should ideally not be obscured by any spoof-attack-related objects (\eg, paper, or masks) and are assumed to yield zero spoof cue maps, we first propose an effective language-guided spoof cue map estimation to enhance one-class FAS models by simulating whether the underlying faces are covered by attack-related objects and generating corresponding nonzero spoof cue maps. Next, we introduce a novel prompt-driven liveness feature disentanglement to alleviate live/spoof-irrelative domain variations by disentangling live/spoof-relevant and domain-dependent information. Finally, we design an effective augmentation strategy by fusing latent features from live images and spoof prompts to generate spoof-like image features and thus diversify latent spoof features to facilitate the learning of one-class FAS. Our extensive experiments and ablation studies support that SLIP consistently outperforms previous one-class FAS methods.

Face anti-spoofing (FAS) aims to distinguish live images and facial spoof attacks to defend facial recognition systems. Thanks to advancements in deep learning, recent deep learning-based FAS methods have shown promising potential, especially in effectively addressing newly developed attacks. In this survey, we first provide an overview of common challenges in FAS and then recap recent advances in deep learning-based FAS. In particular, these anti-spoofing methods generally fall into two main categories, i.e., two-class FAS and one-class FAS. Recent two-class FAS methods have employed a wide range of techniques in developing FAS models, including auxiliary supervision, local descriptor-enhanced feature learning, disentangled feature learning, meta learning, adversarial learning, data augmentation, long-range dependency learning, and multimodal learning. Meanwhile, recent one-class FAS methods have utilized reconstruction supervision, statistical learning, and generative feature learning to learn liveness features solely from live images. In this survey, we also provide an overview of publicly available FAS datasets. Finally, we summarize recent FAS development and highlight some potential future research directions.

Face anti-spoofing (FAS) aims to counter facial presentation attacks and heavily relies on identifying live/spoof discriminative features. While vision transformer (ViT) has shown promising potential in recent FAS methods, there remains a lack of studies examining the values of incorporating local descriptive feature learning with ViT. In this paper, we propose a novel LDCformer by incorporating Learnable Descriptive Convolution (LDC) with ViT and aim to learn distinguishing characteristics of FAS through modeling long-range dependency of locally descriptive features. In addition, we propose to extend LDC to a Decoupled Learnable Descriptive Convolution (Decoupled-LDC) for improving the optimization efficiency. With the new Decoupled-LDC, we further develop an extended model LDCformer ^D for FAS. Extensive experiments on FAS benchmarks show that LDCformer ^D outperforms previous methods on most of the protocols in both intra-domain and cross-domain testings. The codes are available at https://github.com/Pei-KaiHuang/ICIP23_D-LDCformer.

Remote Photoplethysmography (rPPG) aims to measure physiological signals and Heart Rate (HR) from facial videos. Recent unsupervised rPPG estimation methods have shown promising potential in estimating rPPG signals from facial regions without relying on ground truth rPPG signals. However, these methods seem oblivious to interference existing in rPPG signals and still result in unsatisfactory performance. In this paper, we propose a novel De-interfered and Descriptive rPPG Estimation Network (DD-rPPGNet) to eliminate the interference within rPPG features for learning genuine rPPG signals. First, we investigate the characteristics of local spatial-temporal similarities of interference and design a novel unsupervised model to estimate the interference. Next, we propose an unsupervised de-interfered method to learn genuine rPPG signals with two stages. In the first stage, we estimate the initial rPPG signals by contrastive learning from both the training data and their augmented counterparts. In the second stage, we use the estimated interference features to derive de-interfered rPPG features and encourage the rPPG signals to be distinct from the interference. In addition, we propose an effective descriptive rPPG feature learning by developing a strong 3D Learnable Descriptive Convolution (3DLDC) to capture the subtle chrominance changes for enhancing rPPG estimation. Extensive experiments conducted on five rPPG benchmark datasets demonstrate that the proposed DD-rPPGNet outperforms previous unsupervised rPPG estimation methods and achieves competitive performances with state-of-the-art supervised rPPG methods. The code is available at: https://github.com/Pei-KaiHuang/TIFS2025-DD-rPPGNet.