406 citations · 2.5k across the 53 of their papers we have counts for
13 papers · 1 filter
Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing
Yaojie Liu, Xiaoming Liu
Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed "spoof trace", e.g., color distortion, 3D mask edge, Moire pattern, and many others.…
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
Mehdi Bahri, Eimear O' Sullivan, Shunwang Gong +4
Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches…
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images
Debayan Deb, Xiaoming Liu, Anil K. Jain
Prevailing defense mechanisms against adversarial face images tend to overfit to the adversarial perturbations in the training set and fail to generalize to unseen adversarial atta…
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
Feng Liu, Xiaoming Liu
The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a…
Kinematic 3D Object Detection in Monocular Video
Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu +1
Perceiving the physical world in 3D is fundamental for self-driving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and de…
On Disentangling Spoof Trace for Generic Face Anti-Spoofing
Yaojie Liu, Joel Stehouwer, Xiaoming Liu
Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed "spoof trace", e.g., color distortion, 3D mask edge, Moire pattern, and many others.…
Mitigating Face Recognition Bias via Group Adaptive Classifier
Sixue Gong, Xiaoming Liu, Anil K. Jain
Face recognition is known to exhibit bias - subjects in a certain demographic group can be better recognized than other groups. This work aims to learn a fair face representation,…
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Abhinav Kumar, Tim K. Marks, Wenxuan Mou +6
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted loca…
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
Yuge Huang, Yuhan Wang, Ying Tai +5
As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recent…
The Edge of Depth: Explicit Constraints between Segmentation and Depth
Shengjie Zhu, Garrick Brazil, Xiaoming Liu
In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images. For example, to help unsuperv…
Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing
Joel Stehouwer, Amin Jourabloo, Yaojie Liu +1
Using printed photograph and replaying videos of biometric modalities, such as iris, fingerprint and face, are common attacks to fool the recognition systems for granting access as…
Camera Trace Erasing
Chang Chen, Zhiwei Xiong, Xiaoming Liu +1
Camera trace is a unique noise produced in digital imaging process. Most existing forensic methods analyze camera trace to identify image origins. In this paper, we address a new l…
Improving Face Recognition from Hard Samples via Distribution Distillation Loss
Yuge Huang, Pengcheng Shen, Ying Tai +5
Large facial variations are the main challenge in face recognition. To this end, previous variation-specific methods make full use of task-related prior to design special network l…