Search Results for author: Rizhao Cai

Found 20 papers, 7 papers with code

Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis

no code implementations4 Sep 2024 Rizhao Cai, Cecelia Soh, Zitong Yu, Haoliang Li, Wenhan Yang, Alex Kot

While recent FAS works are mainly model-centric, focusing on developing domain generalization algorithms for improving cross-domain performance, data-centric research for face anti-spoofing, improving generalization from data quality and quantity, is largely ignored.

Domain Generalization Face Anti-Spoofing +1

Controllable and Gradual Facial Blemishes Retouching via Physics-Based Modelling

no code implementations19 Jun 2024 Chenhao Shuai, Rizhao Cai, Bandara Dissanayake, Amanda Newman, Dayan Guan, Dennis Sng, Ling Li, Alex Kot

Face retouching aims to remove facial blemishes, such as pigmentation and acne, and still retain fine-grain texture details.

BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models

2 code implementations5 Dec 2023 Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, Alex Kot

Large Multimodal Models (LMMs) such as GPT-4V and LLaVA have shown remarkable capabilities in visual reasoning with common image styles.

Benchmarking Visual Question Answering +1

Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer

1 code implementation20 Sep 2023 Anwei Luo, Rizhao Cai, Chenqi Kong, Yakun Ju, Xiangui Kang, Jiwu Huang, Alex C. Kot

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains.

Face Swapping

Hyperbolic Face Anti-Spoofing

no code implementations17 Aug 2023 Shuangpeng Han, Rizhao Cai, Yawen Cui, Zitong Yu, Yongjian Hu, Alex Kot

To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks.

Contrastive Learning Face Anti-Spoofing +1

Visual Prompt Flexible-Modal Face Anti-Spoofing

no code implementations26 Jul 2023 Zitong Yu, Rizhao Cai, Yawen Cui, Ajian Liu, Changsheng chen

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems.

Face Anti-Spoofing

Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less

no code implementations ICCV 2023 Rizhao Cai, Yawen Cui, Zhi Li, Zitong Yu, Haoliang Li, Yongjian Hu, Alex Kot

To alleviate the forgetting of previous domains without using previous data, we propose the Proxy Prototype Contrastive Regularization (PPCR) to constrain the continual learning with previous domain knowledge from the proxy prototypes.

Continual Learning Domain Generalization +1

Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters

1 code implementation12 Feb 2023 Yawen Cui, Zitong Yu, Rizhao Cai, Xun Wang, Alex C. Kot, Li Liu

The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current FSCL methods are all for the class-incremental purpose.

Continual Learning Contrastive Learning +2

Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing

no code implementations11 Feb 2023 Zitong Yu, Rizhao Cai, Yawen Cui, Xin Liu, Yongjian Hu, Alex Kot

In this paper, we investigate three key factors (i. e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth.

Face Anti-Spoofing

Toward domain generalized pruning by scoring out-of-distribution importance

no code implementations25 Oct 2022 Rizhao Cai, Haoliang Li, Alex Kot

Filter pruning has been widely used for compressing convolutional neural networks to reduce computation costs during the deployment stage.

Domain Generalization

Forensicability Assessment of Questioned Images in Recapturing Detection

no code implementations5 Sep 2022 Changsheng chen, Lin Zhao, Rizhao Cai, Zitong Yu, Jiwu Huang, Alex C. Kot

We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.

Face Anti-Spoofing Image Quality Assessment

Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues

no code implementations10 Aug 2022 Zitong Yu, Rizhao Cai, Zhi Li, Wenhan Yang, Jingang Shi, Alex C. Kot

In this paper, we establish the first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues.

Benchmarking DeepFake Detection +3

One-Class Knowledge Distillation for Face Presentation Attack Detection

2 code implementations8 May 2022 Zhi Li, Rizhao Cai, Haoliang Li, Kwok-Yan Lam, Yongjian Hu, Alex C. Kot

Under this framework, a teacher network is trained with source domain samples to provide discriminative feature representations for face PAD.

Face Presentation Attack Detection

Automatic Facial Skin Feature Detection for Everyone

no code implementations30 Mar 2022 Qian Zheng, Ankur Purwar, Heng Zhao, Guang Liang Lim, Ling Li, Debasish Behera, Qian Wang, Min Tan, Rizhao Cai, Jennifer Werner, Dennis Sng, Maurice van Steensel, Weisi Lin, Alex C Kot

We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild.

Product Recommendation

Learning Meta Pattern for Face Anti-Spoofing

1 code implementation13 Oct 2021 Rizhao Cai, Zhi Li, Renjie Wan, Haoliang Li, Yongjian Hu, Alex ChiChung Kot

To improve the generalization ability, recent hybrid methods have been explored to extract task-aware handcrafted features (e. g., Local Binary Pattern) as discriminative information for the input of DNNs.

Domain Generalization Face Anti-Spoofing +1

DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

no code implementations16 Sep 2020 Rizhao Cai, Haoliang Li, Shiqi Wang, Changsheng chen, Alex ChiChung Kot

Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i. e., to glance at the example globally first and then carefully observe the local regions to gain more discriminative information, for the face anti-spoofing problem, we propose a novel framework based on the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN).

Deep Reinforcement Learning Face Anti-Spoofing +2

Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing

no code implementations9 Oct 2019 Rizhao Cai, Changsheng chen

Studies about the transferability of the adversarial attack reveal that utilizing handcrafted feature-based methods could improve security in a system-level.

Adversarial Attack Face Anti-Spoofing +1

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