An Effective Approach to Pose Invariant 3D Face Recognition

Abstract. One critical challenge encountered by existing face recognition techniques lies in the difficulties of handling varying poses. In this paper, we propose a novel pose invariant 3D face recognition scheme to improve regular face recognition from two aspects. Firstly, we propose an effective geometry based alignment approach, which transforms a 3D face mesh model to a well-aligned 2D image. Secondly, we propose to represent the facial images by a Locality Preserving Sparse Coding (LPSC) algorithm, which is more effective than the regular sparse coding algorithm for face representation. We conducted a set of extensive experiments on both 2D and 3D face recognition, in which the encouraging results showed that the proposed scheme is more effective than the regular face recognition solutions.

Robust 3D Face Recognition from Expression Categorisation

The task of Face Recognition is often cited as being complicated by the presence of lighting and expression variation. In this article a novel combination of facial expression categorisation and 3D Face Recognition is used to provide enhanced recognition performance. The use of 3D face data alleviates performance issues related to pose and illumination. Part-face decomposition is combined with a novel adaptive weighting scheme to increase robustness to expression variation. By using local features instead of a monolithic approach, this system configuration allows for expression variability to be modelled and aid in the fusion process. The system is tested on the Face Recognition Grand Challenge (FRGC) database, currently the largest available dataset of 3D faces. The sensitivity of the proposed approach is also evaluated in the presence of systematic error in the expression classification stage

Local absolute binary patterns as image preprocessing for grip-pattern recognition in smart gun

In a biometric verification system of a smart gun, the rightful user is recognized based on his handpressure pattern. The main factor which affects the verification performance of this system is the variation between the probe image and the gallery image of a subject, in particular when the probe and the gallery images have been recorded with a few weeks in between. One of the major variations is in the pressure distribution of images. In this work, we propose a novel preprocessing technique, Local Absolute Binary Patterns, prior to grippattern classification. With respect to a certain pixel in an image, Local Absolute Binary Patterns processing quantifies how its neighboring pixels are fluctuating. It will be shown that this technique can both reduce the variation of pressure distribution, and extract information of the hand shape in the image. Therefore, a significant improvement of the verification result has been achieved