no code implementations • 4 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.
no code implementations • 19 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.
2 code implementations • CVPR 2024 • Xun Lin, Shuai Wang, Rizhao Cai, Yizhong Liu, Ying Fu, Zitong Yu, Wenzhong Tang, Alex Kot
Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks.
2 code implementations • 5 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.
Ranked #1000000000 on
Visual Question Answering
on MS COCO
1 code implementation • 20 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.
3 code implementations • 7 Sep 2023 • Rizhao Cai, Zitong Yu, Chenqi Kong, Haoliang Li, Changsheng chen, Yongjian Hu, Alex Kot
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces.
no code implementations • 17 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.
no code implementations • 26 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.
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.
no code implementations • 23 Feb 2023 • Ling Li, Bandara Dissanayake, Tatsuya Omotezako, Yunjie Zhong, Qing Zhang, Rizhao Cai, Qian Zheng, Dennis Sng, Weisi Lin, YuFei Wang, Alex C Kot
In this paper, we propose the first simulation model to reveal facial pore changes after using skincare products.
1 code implementation • 12 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.
no code implementations • 11 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.
no code implementations • 25 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.
no code implementations • 5 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.
no code implementations • 10 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.
2 code implementations • 8 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.
no code implementations • 30 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.
1 code implementation • 13 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.
no code implementations • 16 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).
no code implementations • 9 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.