A new scheme for 3D face recognition

A novel system for 3D face recognition is presented in this paper. Firstly, we reduce the noise and move spikes from all the 3D faces. Secondly, we use Iterative Closet Point (ICP) to align all 3D face with the first person, and then for each face, we find the nose tip. Once the nose tip is successfully found, we crop a region, which is defined by a sphere radius of 100 mm centered at the nose tip. Depth image are constructed using the region subsequently. Then the depth image is projected into Gabor-based Supervised Locality Sensitive Discriminant Analysis (GISLSDA) space, which is improved by Gabor wavelet and Two-Directional Two Dimensions Principal Component Analysis (2D2PCA). Recognition is achieved by using a Nearest Neighbor (NN) classifier finally. This method is robust to changes in facial expressions and poses. The experimental results show that the new algorithm outperforms the other popular approaches reported in the literature and achieves much higher accurate recognition rate.

[1]  Jae-Chang Shim,et al.  Curvature based human face recognition using depth weighted Hausdorff distance , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[2]  Kun Zhou,et al.  Locality Sensitive Discriminant Analysis , 2007, IJCAI.

[3]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Zhaohui Wu,et al.  Automatic 3D face verification from range data , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[5]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[7]  Feng Han,et al.  3D human face recognition using point signature , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[8]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[9]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[10]  Tieniu Tan,et al.  Robust 3D Face Recognition Using Learned Visual Codebook , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[12]  Patrick J. Flynn,et al.  An evaluation of multimodal 2D+3D face biometrics , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Mark W. Koch,et al.  A 2D Range Hausdorff Approach for 3D Face Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[14]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[15]  Marc Acheroy,et al.  Face verification from 3D and grey level clues , 2001, Pattern Recognit. Lett..

[16]  Xiaolong Teng,et al.  Face recognition using discriminant locality preserving projections , 2006, Image Vis. Comput..