Non-negative dictionary based sparse representation classification for ear recognition with occlusion

Neurocomputing

Volumn 171, Issue , 2016, Pages 540-550

  Yuan, Li ^a   Liu, Wei ^a   Li, Yang ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^b State Key Laboratory of Media Convergence Production Technology and Systems   (China)

Author keywords

Ear recognition with occlusion; Non negative occlusion dictionary; Sparse representation classification

Indexed keywords

NEURAL NETWORKS;

CLASSIFICATION SCHEME; COMPUTATIONAL LOADS; DISCRIMINATION ABILITY; EAR RECOGNITION; NON NEGATIVES; PARTIAL OCCLUSIONS; SPARSE REPRESENTATION; SPARSE REPRESENTATION BASED CLASSIFICATIONS;

ATOMS;

ARTICLE; BOOK; CLASSIFICATION; COMPARATIVE STUDY; DATA BASE; EAR; EAR RECOGNITION; LEARNING; MATHEMATICAL ANALYSIS; NON NEGATIVE OCCLUSION DICTIONARY; PATTERN RECOGNITION; PRIORITY JOURNAL; SPARSE REPRESENTATION CLASSIFICATION;

EID: 84947043922     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.06.074     Document Type: Article

References (30)

1. Kumar A., Chan T.T. Robust ear identification using sparse representation of local texture descriptors. Pattern Recog. 2013, 46:73-85.
2. Chen H., Bhanu B. Efficient recognition of highly similar 3D objects in range images. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(1):172-179.
3. Bustard J., Nixon M. Toward unconstrained ear recognition from two-dimensional images. IEEE Trans. Syst., Man Cybern, A: Syst. Hum. 2010, 40(3):486-494.
4. Abaza A., Ross A., Hebert C., Harrison M., Nixon.M. A survey on ear biometrics. ACM Trans. Embed. Comput. Syst. 2011, 9(4):1-33. Article 39.
5. Mu Z., Yuan L., Xu Z., Xi D., Qi S. Shape and structural feature based ear recognition. Proceedings of sinobiometrics 2004. Lect. Notes Comput. Sci. 2004, V3338:663-670.
6. L. Lu, X. Zhang, Y. Zhao, Y. Jia, Ear recognition based on statistical shape model, in: Proceedings of the First International Conference on Innovative Computing, Information and Control, ICICIC'06, 2006.
7. Bustard D., Nixon M.S. Towards unconstrained ear recognition from two dimensional images. IEEE Trans. Syst., Man, Cybern., A 2010, 40(3):486-494.
8. Chang K., Victor B., Bowyer K.W., Sarkar S. Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25(8):1160-1165.
9. Xiaoxun Z., Yunde J. Symmetrical null space for face and ear recognition. Neurocomputing 2007, 70:842-848.
10. Arbab-Zavar B., Nixon M. On guided model-based analysis of ear biometrics. Comput. Vis. Image Underst. 2011, 115:487-502.
11. Nanni L., Lumini A. A multi-matcher model for ear recognition. Pattern Recog. 2007, 28:2216-2219.
12. Kumar A., Wu Chenye Automated human identification using ear imaging. Pattern Recog. 2012, 45:956-968.
13. Y. Wang, Z. Mu, H. Zheng, Block-based and multiresolution methods for ear recognition using wavelet transform and uniform local binary pattern, in: Proceedings of the ICPR'08, ICPR, 2008.
14. Chan T.S., Kumar A. Reliable ear identification using 2D quadrature filters. Pattern Recog. Lett. 2012, 33(14):1870-1881.
15. L. Yuan, Z.C. Mu, Y. Zhang. Ear recognition using improved non-negative matrix factorization, in: Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, August 2006, pp. 501-504.
16. Yuan L., Mu Z.C., Zeng H. Partially occluded ear recognition based on local features (in Chinese). J. Univ. Sci. Technol. Beijing 2010, 32(4):530-535.
17. Yuan L., Mu Z.C. Ear recognition based on local information fusion. Pattern Recog. Lett. 2012, 33(2):182-190.
18. He R., Zheng W.S., Hu B.G. Maximum correntropy criterion for robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(8):1561-1576.
19. L. Yuan, C. Li, Z.C. Mu. Ear recognition under partial occlusion based on sparse representation, in: Proceedings of the 2012 International Conference on System Science and Engineering, Dalian, China, 2012, pp. 349-352.
20. Huang Z.X., Liu Y.G., Li C.G., Yang M.L., Chen.L.P. A robust face and ear based multimodal biometric system using sparse representation. Pattern Recog. 2013, 46(8):2156-2168.
21. Zhang B.Q., Mu Z.C., Li C., Zeng H. Robust classification for occluded ear via gabor scale feature-based non-negative sparse representation. Opt. Eng. 2014, 53(6):061702. 1-10.
22. Wright J., Ma Y., et al. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(2):210-227.
23. Yang M., Zhang L., Shiu S.C.K., Zhang.D. Gabor feature based robust representation and classification for face recognition with gabor occlusion dictionary. Pattern Recog. 2013, 46(6):1865-1878.
24. He R., Zheng W.S., Tan T.N., Sun.Z.N. Half-quadratic-based iterative minimization for robust sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2014, 36(2):261-275.
25. Hoyer P.O. Non-negative sparse coding. Proceedings of 12th IEEE Workshop on Neural Networks for Signal Processing 2002, 557-565. Martigny, Switzerland.
26. Liu C., Wechsler.H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 2002, 11(4):273-292.
27. Lei Z., Chu R.F., He R., Liao S.C., Li S.Z. Face Lecture Notes in Computer Science. Proceedings of 2007 International Conference on Advances in Biometrics 2007, vol. 4642:87-95. Seoul, Korea.
28. http://www.ustb.edu.cn/resh/.
29. Lee D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401(6755):788-791.
30. Hoyer P.O. Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 2004, 5(9):1457-1469.