About

Who We Are

This is a group photo of our laboratory members. Young and active, we are full of passion and energy, enjoy the works we do, love the laboratory we live.

Research Highlights

We focus on the research of theories, algorithms, and systems of biometrics, with applications to video surveillance, identity management, mobile and cyber security, and smart healthcare.

  • 3D Face Reconstruction
  • 3D/RGB-D Face Processing & Recognition
  • 3D Fingerprint Modeling & Recognition
  • 3D Biometric Data Quality & Individuality
  • 2D/3D Face Detection & Alignment
  • Facial Attribute Analysis
  • Deep Face Representations
  • Face Anti-Spoofing
  • Statistical Fingerprint Feature Models
  • Latent/Partial Fingerprint Processing & Recognition
  • Fingerprint Quality Assessments
  • Fingerprint Liveness Detection
  • Pedestrian Detection & Re-Identification
  • Long-distance Object Detection & Classification
  • Action & Event Recognition
  • Facial Expression Recognition
  • Multi-Modality-Based Emotion Recognition

Welcome to BRL

BRL

Institute of Image & Graphics, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu , China

+8628 85417865

Projects

3D Face Reconstruction and Recognition

Given single or multiple 2D face images of arbitrary poses and expressions, we aim to reconstruct 3D face models and apply them to enhance unconstrained face recognition accuracy.

Anti-spoofing for Securing Biometric Systems

Most existing biometric systems are fragile to spoofing attacks with fake biometric data. We seek for effective appearance features to distinguish fake from real biometric data via image analysis and machine learning methods.

Learning-based Fingerprint Analysis and Processing

Robust fingerprint image processing is critical to reliable and accurate automated fingerprint recognition. We apply statistical and deep learning methods to model fingerprint featrues and pre-process fingerprint images.

Publications

On 3D Face Reconstruction via Cascaded Regression in Shape Space

Cascaded regression has been recently applied to reconstructing 3D faces from single 2D images directly in shape space, and achieved state-of-the-art performance. This paper investigates thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well studied yet: (i) The impact of the number of 2D landmarks; (ii) the impact of the number of 3D vertices; (iii) the way of using standalone automated landmark detection methods; and (iv) the convergence property. To answer these questions, ...

2.5D Cascaded Regression for Robust Facial Landmark Detection

In this paper, we propose a 2.5D Cascaded Regression approach for accurately and robustly locating facial landmarks on RGB-D data. Instead of detecting facial landmarks on texture and depth images separately, the proposed method alternately applies depth-based and texture-based regressors to compute the necessary increments to the estimated landmarks so that they are gradually moved towards their true positions. This way, depth information is better explored through close interaction with texture information, and they together ...

Multi-Dim: A Multi-Dimensional Face Database Towards the Application of 3D Technology in Real-World Scenarios

Three-dimensional (3D) faces are increasingly utilized in many face-related tasks. Despite the promising improvement achieved by 3D face technology, it is still hard to thoroughly evaluate the performance and effect of 3D face technology in real-world applications where variations frequently occur in pose, illumination, expression and many other factors. This is due to the lack of benchmark databases that contain both high precision full-view 3D faces and their 2D face images/videos under different conditions. In this paper, we present such a multi-dimensional face database (namely Multi-Dim) of ...

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