A novel scheme for face recognition and authentication with illumination and expression changes

In this paper, a novel scheme is proposed for face recognition or authentication against illumination, and expression variation using multimodal face features. First, a sub -image in low-frequency sub-band is extracted by a wavelet-based transform to preserve the invariant data and reduce the dimensionality. The reduced sub-image LL is partitioned into four parts for representing local features and reducing illumination effect. Sub-image LL is reduced again to obtain a full face in a smaller scale for globally representing the whole face image. Five modal feature spaces are constructed. The most discriminant common vectors (DCVs) for each classifier are found. A weighted summation is performed to fuse the five classified results. Experimental results were conducted to show that the proposed scheme is superior to other methods in terms of recognition rate and authentication rate.

[1]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Meng Joo Er,et al.  High-speed face recognition based on discrete cosine transform and RBF neural networks , 2005, IEEE Transactions on Neural Networks.

[3]  Jeff Fortuna,et al.  Improved support vector classification using PCA and ICA feature space modification , 2004, Pattern Recognit..

[4]  Chengjun Liu,et al.  Gabor-based kernel PCA with fractional power polynomial models for face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  B. Achiriloaie,et al.  VI REFERENCES , 1961 .

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

[8]  Halim Fathoni,et al.  DEPARTMENT OF COMPUTER SCIENCE AND INFORMATION ENGINEERING , 2008 .

[9]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Young Gil Kim,et al.  Face recognition robust to left/right shadows; facial symmetry , 2006, Pattern Recognit..

[11]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[12]  Jian Yang,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[15]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Rama Chellappa,et al.  Background learning for robust face recognition with PCA in the presence of clutter , 2005, IEEE Transactions on Image Processing.

[17]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[18]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Josef Kittler,et al.  Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Jen-Tzung Chien,et al.  Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Ravi Kothari,et al.  Fractional-Step Dimensionality Reduction , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Amnon Shashua,et al.  The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.