Thresholded Two-Phase Test Sample Representation for Outlier Rejection in Biological Recognition

The two-phase test sample representation (TPTSR) was proposed as a useful classifier for face recognition. However, the TPTSR method is not able to reject the impostor, so it should be modified for real-world applications. This paper introduces a thresholded TPTSR (T-TPTSR) method for complex object recognition with outliers, and two criteria for assessing the performance of outlier rejection and member classification are defined. The performance of the T-TPTSR method is compared with the modified global representation, PCA and LDA methods, respectively. The results show that the T-TPTSR method achieves the best performance among them according to the two criteria.

[1]  C. Cattani,et al.  Markov Models for Image Labeling , 2012 .

[2]  Jian Yang,et al.  Sparse Approximation to the Eigensubspace for Discrimination , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Shengyong Chen,et al.  Biomedical Signal Processing and Modeling Complexity of Living Systems 2014 , 2015, Comput. Math. Methods Medicine.

[4]  Dong Xu,et al.  Multilinear Discriminant Analysis for Face Recognition , 2007, IEEE Transactions on Image Processing.

[5]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..

[6]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[8]  Jonathan Gillard,et al.  Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI , 2012, Comput. Math. Methods Medicine.

[9]  Jian Yang,et al.  A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  David Zhang,et al.  Represent and fuse bimodal biometric images at the feature level: complex-matrix-based fusion scheme , 2010 .

[11]  Jian Yang,et al.  Sparse Local Discriminant Projections for Feature Extraction , 2010, 2010 20th International Conference on Pattern Recognition.

[12]  Marios Savvides,et al.  A Multifactor Extension of Linear Discriminant Analysis for Face Recognition under Varying Pose and Illumination , 2010, EURASIP J. Adv. Signal Process..

[13]  Yen-Lun Chen,et al.  Face recognition for target detection on PCA features with outlier information , 2007, 2007 50th Midwest Symposium on Circuits and Systems.

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

[15]  Yu Shi,et al.  Sparse discriminant analysis for breast cancer biomarker identification and classification , 2009 .

[16]  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.

[17]  Lei Xu,et al.  Improved system for object detection and star/galaxy classification via local subspace analysis , 2003, Neural Networks.

[18]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[20]  Shengyong Chen,et al.  Biomedical Signal Processing and Modeling Complexity of Living Systems , 2012, Comput. Math. Methods Medicine.

[21]  Zhong Jin,et al.  Sparse Local Discriminant Projections for Face Feature Extraction , 2010 .

[22]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Jennifer G. Dy,et al.  Using Local Dependencies within Batches to Improve Large Margin Classifiers , 2009, J. Mach. Learn. Res..

[24]  Jian Yang,et al.  An approach for directly extracting features from matrix data and its application in face recognition , 2008, Neurocomputing.

[25]  Dacheng Tao,et al.  Kernel full-space biased discriminant analysis , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[26]  Thomas S. Huang,et al.  Robust estimation of foreground in surveillance videos by sparse error estimation , 2008, 2008 19th International Conference on Pattern Recognition.

[27]  Shengyong Chen,et al.  Modeling of Biological Intelligence for SCM System Optimization , 2011, Comput. Math. Methods Medicine.

[28]  Zhong Jin,et al.  Down-Sampling Face Images and Low-Resolution Face Recognition , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[29]  Shengyong Chen,et al.  Functional Magnetic Resonance Imaging for Imaging Neural Activity in the Human Brain: The Annual Progress , 2012, Comput. Math. Methods Medicine.

[30]  Yi Yang,et al.  Image Clustering Using Local Discriminant Models and Global Integration , 2010, IEEE Transactions on Image Processing.

[31]  David Zhang,et al.  Local Linear Discriminant Analysis Framework Using Sample Neighbors , 2011, IEEE Transactions on Neural Networks.

[32]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[33]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.