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Robust Face Detection System

This work is aiming to develop a real-time, robust, viewpoint and illumination invariant face detection system.  The system may work in the presence of occlusion and clutter.

3D Face Recognition in the Presence of Expression: A Guidance-based
Constraint Deformation Approach
Yueming Wang, Gang Pan, Zhaohui Wu

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'07)

Three-dimensional human face recognition in the presence of expression is a big challenge, since the shape distortion caused by facial expression greatly weakens the rigid matching. This paper proposes a guidance-based constraint deformation(GCD) model to cope with the shape distortion by expression. The basic idea is that, the face model with non-neutral expression is deformed toward its neutral one under certain constraint so that the distortion is reduced while inter-class discriminative information is preserved. The GCD model exploits the neutral 3D face shape to guide the deformation, meanwhile applies a rigid constraint on it. Both steps are smoothly unified in the Poisson equation framework. The GCD approach only needs one neutral model for each person in the gallery. The experimental results, carried out on the large 3D face databases-FRGC v2.0, demonstrate that our method significantly outperforms ICP method for both identification and authentication mode. It shows the GCD model is promising for coping with the shape distortion in 3D face recognition.

Super-Resolution of 3D Face
Gang Pan, Shi Han, Zhaohui Wu, and Yueming Wang

The 9th European Conference on Computer Vision (ECCV'06)

Super-resolution is a technique to restore the detailed information from the degenerated data. Lots of previous work is for 2D images while super-resolution of 3D models was little addressed. This paper focuses on the super-resolution of 3D human faces. We firstly extend the 2D image pyramid model to the progressive resolution chain (PRC) model in 3D domain, to describe the detail variation during resolution decreasing. Then a consistent planar representation of 3D faces is presented, which enables the analysis and comparison among the features of the same facial part for the subsequent restoration process. Finally, formulated as
solving an iterative quadratic system by maximizing a posteriori, a 3D restoration algorithm using PRC features is given. The experimental results on USF HumanID 3D face database demonstrate the effectiveness of the proposed approach.