41 citations · 130 across the 24 of their papers we have counts for
25 papers
SapiensID 2.0: Aligning Human Recognition Foundation Models with Human Perception
Yiyang Su, Jie Zhu, Feng Liu +2
While foundation models have significantly advanced human recognition across diverse modalities, they predominantly rely on static, geometric feature extraction. This approach fund…
Non-Colliding Biometric Identities for Digital Entities: Geometry, Capacity, and Million-Scale Virtual Identity Provisioning
Yuyang Ji, Yixuan Shen, Anil Jain +2
Digital entities such as AI agents and humanoid robots increasingly operate alongside real humans, yet their identity infrastructure is based on credentials rather than embodied bi…
LocalScore: Local Density-Aware Similarity Scoring for Biometrics
Yiyang Su, Minchul Kim, Jie Zhu +4
Open-set biometrics faces challenges with probe subjects who may not be enrolled in the gallery, as traditional biometric systems struggle to detect these non-mated probes. Despite…
Person Recognition in Aerial Surveillance: A Decade Survey
Kien Nguyen, Feng Liu, Clinton Fookes +3
The rapid emergence of airborne platforms and imaging sensors is enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment, and…
HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID
Yiyang Su, Yunping Shi, Feng Liu +1
Recently, research interest in person re-identification (ReID) has increasingly focused on video-based scenarios, which are essential for robust surveillance and security in varied…
SapiensID: Foundation for Human Recognition
Minchul Kim, Dingqiang Ye, Yiyang Su +2
Existing human recognition systems often rely on separate, specialized models for face and body analysis, limiting their effectiveness in real-world scenarios where pose, visibilit…
Open-Set Biometrics: Beyond Good Closed-Set Models
Yiyang Su, Minchul Kim, Feng Liu +2
Biometric recognition has primarily addressed closed-set identification, assuming all probe subjects are in the gallery. However, most practical applications involve open-set biome…
KeyPoint Relative Position Encoding for Face Recognition
Minchul Kim, Yiyang Su, Feng Liu +2
In this paper, we address the challenge of making ViT models more robust to unseen affine transformations. Such robustness becomes useful in various recognition tasks such as face…
INFAMOUS-NeRF: ImproviNg FAce MOdeling Using Semantically-Aligned Hypernetworks with Neural Radiance Fields
Andrew Hou, Feng Liu, Zhiyuan Ren +4
We propose INFAMOUS-NeRF, an implicit morphable face model that introduces hypernetworks to NeRF to improve the representation power in the presence of many training subjects. At t…
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification
Feng Liu, Minchul Kim, ZiAng Gu +2
Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics. In this work, we aim to extend LT-ReID beyond pedestrian recognition…
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Feng Liu, Ryan Ashbaugh, Nicholas Chimitt +17
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to…
DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
Minchul Kim, Feng Liu, Anil Jain +1
Generating synthetic datasets for training face recognition models is challenging because dataset generation entails more than creating high fidelity images. It involves generating…
Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects
Feng Liu, Xiaoming Liu
The objective of this paper is to learn dense 3D shape correspondence for topology-varying generic objects in an unsupervised manner. Conventional implicit functions estimate the o…
Cluster and Aggregate: Face Recognition with Large Probe Set
Minchul Kim, Feng Liu, Anil Jain +1
Feature fusion plays a crucial role in unconstrained face recognition where inputs (probes) comprise of a set of low quality images whose individual qualities vary. Advances in…
2D GANs Meet Unsupervised Single-view 3D Reconstruction
Feng Liu, Xiaoming Liu
Recent research has shown that controllable image generation based on pre-trained GANs can benefit a wide range of computer vision tasks. However, less attention has been devoted t…
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
Feng Liu, Minchul Kim, Anil Jain +1
Although significant advances have been made in face recognition (FR), FR in unconstrained environments remains challenging due to the domain gap between the semi-constrained train…
The State of Aerial Surveillance: A Survey
Kien Nguyen, Clinton Fookes, Sridha Sridharan +4
The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and…
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…
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…
Local Facial Makeup Transfer via Disentangled Representation
Zhaoyang Sun, Wenxuan Liu, Feng Liu +2
Facial makeup transfer aims to render a non-makeup face image in an arbitrary given makeup one while preserving face identity. The most advanced method separates makeup style infor…
On the Detection of Digital Face Manipulation
Hao Dang, Feng Liu, Joel Stehouwer +2
Detecting manipulated facial images and videos is an increasingly important topic in digital media forensics. As advanced face synthesis and manipulation methods are made available…
On Learning Disentangled Representations for Gait Recognition
Ziyuan Zhang, Luan Tran, Feng Liu +1
Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models a…
Towards High-fidelity Nonlinear 3D Face Morphable Model
Luan Tran, Feng Liu, Xiaoming Liu
Embedding 3D morphable basis functions into deep neural networks opens great potential for models with better representation power. However, to faithfully learn those models from a…
3D Face Modeling From Diverse Raw Scan Data
Feng Liu, Luan Tran, Xiaoming Liu
Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database. The main roadblock of building a large-scale face…