Face recognition using spatially smoothed discriminant structure-preserved projections

Abstract. Recently, structure-preserved projections (SPP) were proposed as a local matching-based algorithm for face recognition. Compared with other methods, the main advantage of SPP is that it can preserve the configural structure of subpatterns in each face image. However, the SPP algorithm ignores the information among samples from different classes, which may weaken its recognition performances. Moreover, the relationships of nearby pixels in the subpattern are also neglected in SPP. In order to address these limitations, a new algorithm termed spatially smoothed discriminant structure-preserved projections (SS-DSPP) is proposed. SS-DSPP takes advantage of the class information to characterize the discrimination structure of subpatterns from different classes, and a new spatially smooth constraint is also derived to preserve the intrinsic two-dimensional structure of each subpattern. The feasibility and effectiveness of the proposed algorithm are evaluated on four standard face databases (Yale, extended YaleB, CMU PIE, and AR). Experimental results demonstrate that our SS-DSPP outperforms the original SPP and several state-of-the-art algorithms.

[1]  Jianzhong Wang,et al.  An adaptively weighted sub-pattern locality preserving projection for face recognition , 2010, J. Netw. Comput. Appl..

[2]  Yu-Lian Zhu Sub-pattern non-negative matrix factorization based on random subspace for face recognition , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

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

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

[5]  Deng Cai,et al.  Tensor Subspace Analysis , 2005, NIPS.

[6]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yulian Zhu,et al.  Local ridge regression for face recognition , 2009, Neurocomputing.

[8]  Yun Fu,et al.  Image Classification Using Correlation Tensor Analysis , 2008, IEEE Transactions on Image Processing.

[9]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[10]  Elzbieta Pekalska,et al.  Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Yuxiao Hu,et al.  Learning a Spatially Smooth Subspace for Face Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Pawan Sinha,et al.  Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.

[15]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Loris Nanni,et al.  Weighted Sub-Gabor for face recognition , 2007, Pattern Recognit. Lett..

[17]  Yulian Zhu,et al.  Subpattern-based principle component analysis , 2004, Pattern Recognit..

[18]  Feiping Nie,et al.  Extracting the Optimal Dimensionality for Discriminant Analysis , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[19]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

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

[21]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[22]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[23]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[24]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Hui Xu,et al.  Two-dimensional supervised local similarity and diversity projection , 2010, Pattern Recognit..

[27]  Jianzhong Wang,et al.  A structure-preserved local matching approach for face recognition , 2011, Pattern Recognit. Lett..

[28]  Songcan Chen,et al.  Adaptively weighted sub-pattern PCA for face recognition , 2005, Neurocomputing.

[29]  Miao Qi,et al.  A Supervised Locality Preserving Projections Based Local Matching Algorithm for Face Recognition , 2010, AST/UCMA/ISA/ACN.

[30]  Atul Negi,et al.  SubXPCA and a generalized feature partitioning approach to principal component analysis , 2008, Pattern Recognit..

[31]  Qiang Ji,et al.  A Comparative Study of Local Matching Approach for Face Recognition , 2007, IEEE Transactions on Image Processing.

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

[33]  Dewen Hu,et al.  Comment on: "Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition" , 2008, Pattern Recognit..

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

[35]  Jian Yang,et al.  Sparse Tensor Discriminant Color Space for Face Verification , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[36]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[37]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[38]  Stan Z. Li,et al.  Face recognition using the nearest feature line method , 1999, IEEE Trans. Neural Networks.