Neighborhood discriminant embedding in face recognition

We present a novel feature extraction method for face recog- nition called neighborhood discriminant embedding NDE, which incor- porates graph embedding and Fisher's criterion and includes an indi- vidual discriminative factor IDF. Graph embedding is able to reveal the representative and discriminative features from the underlying nonlinear face data structure. Fisher's criterion is recognized as an effective tech- nique for discriminative feature extraction. IDF is proposed as an indi- vidual property of each sample to describe the contribution to classifica- tion. NDE can remain the local structure of the nearest neighbors of each data point during the dimensionality reduction as well as gather the within-class points and separate the between-class points in the low- dimensional projected space. Utilizing Fisher's criterion and taking into account IDF, the discriminative capability of NDE is further enhanced. Comprehensive experiments are conducted using the Olivetti Research Laboratory ORL and Facial Recognition Technology FERET face da- tabases to demonstrate the effectiveness of our methods. © 2010 Society of

[1]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[2]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

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

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

[5]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[6]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Yuxiao Hu,et al.  Learning a locality preserving subspace for visual recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  I. Jolliffe.  Principal Component Analysis , 2002 .

[9]  Andrew Beng Jin Teoh,et al.  Neighbourhood preserving discriminant embedding in face recognition , 2009, J. Vis. Commun. Image Represent..

[10]  Tat-Seng Chua,et al.  Learning object models from semistructured Web documents , 2006, IEEE Transactions on Knowledge and Data Engineering.

[11]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[12]  Feiping Nie,et al.  Neighborhood MinMax Projections , 2007, IJCAI.

[13]  Shuicheng Yan,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007 .

[14]  Stan Z. Li,et al.  Nonlinear mapping from multi-view face patterns to a Gaussian distribution in a low dimensional space , 2001, Proceedings IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems.

[15]  Hwann-Tzong Chen,et al.  Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[17]  Ming Li,et al.  2D-LDA: A statistical linear discriminant analysis for image matrix , 2005, Pattern Recognit. Lett..

[18]  Tom Minka,et al.  Automatic Choice of Dimensionality for PCA , 2000, NIPS.

[19]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[20]  Aleix M. Martínez,et al.  Where are linear feature extraction methods applicable? , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jieping Ye,et al.  Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis , 2006, IEEE Transactions on Knowledge and Data Engineering.

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

[23]  Tieniu Tan,et al.  Null space-based kernel Fisher discriminant analysis for face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[24]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

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

[26]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[27]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[28]  Masashi Sugiyama,et al.  Local Fisher discriminant analysis for supervised dimensionality reduction , 2006, ICML.

[29]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[30]  Dacheng Tao,et al.  Bregman Divergence-Based Regularization for Transfer Subspace Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.