Combining local face image features for identity verification

Neurocomputing

Volumn 74, Issue 16, 2011, Pages 2452-2463

  Oh, Beom Seok ^a   Toh, Kar Ann ^a   Beng Jin Teoh, Andrew ^a   Kim, Jaihie ^a  

^a Yonsei University   (South Korea)

Author keywords

Extreme learning machine; Face identity verification; Local feature extraction; Match scores fusion; Total error rate minimization

Indexed keywords

EXTREME LEARNING MACHINE; IDENTITY VERIFICATION; LOCAL FEATURE EXTRACTION; MATCH SCORES FUSION; TOTAL ERROR RATE MINIMIZATION;

DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS;

FACE RECOGNITION;

ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOMETRY; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; EXTREME LEARNING MACHINE; FACE; IDENTITY VERIFICATION; INFORMATION PROCESSING; LINEAR DISCRIMINANT ANALYSIS; LOCAL FACE IMAGE FEATURES; MACHINE LEARNING; MATHEMATICAL COMPUTING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL;

EID: 80051577109     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.12.036     Document Type: Article

References (42)

1. Zhao W., Chellappa R., Phillips P., Rosenfeld A. Face recognition: a literature survey. ACM Computing Surveys 2003, 35(4):399-458. (CSUR).
2. Turk M., Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience Winter 1991, 3:71-86.
3. Belhumeur P.N., Hespanha J.P., Kriegman D.J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7):711-720.
4. Gottumukkal R., Asari V.K. An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters 2004, 25(4):429-436.
5. Brunelli R., Poggio T. Face recognition: features versus templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993, 15(10):1042-1052.
6. Heisele B., Serre T., Poggio T. A component-based framework for face detection and identification. International Journal of Computer Vision 2007, 74(2):167-181.
7. Vogel J., Schwaninger A., Wallraven C., Bülthoff H.H. Categorization of natural scenes: local vs global information. Proceedings of the 3rd Symposium on Applied Perception in Graphics and Visualization 2006, 33-40. ACM, New York, NY, USA.
8. Zou J., Ji Q., Nagy G. A comparative study of local matching approach for face recognition. IEEE Transactions on Image Processing 2007, 16(10):2617-2628.
9. Wiskott L., Fellous J.-M., Kuiger N., von der Malsburg C. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7):775-779.
10. Toh K.-A. Deterministic neural classification. Neural Computation 2008, 20(6):1565-1595.
11. Etemad K., Chellappa R. Discriminant analysis for recognition of human face images. Journal of the Optical Society of America A 1997, 14(8):1724-1733.
12. Yang J., Zhang D., Frangi A.F., Yang J.-Y. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(1):131-137.
13. Yang J., Liu C. Horizontal and vertical 2DPCA based discriminant analysis for face verification using the FRGC version 2 database. International Conference on Biometrics 2007, 838-847.
14. Yu W., Wang Z., Chen W. A new framework to combine vertical and horizontal information for face recognition. Neurocomputing 2009, 72(4-6):1084-1091.
15. Ye J., Janardan R., Li Q. Two-dimensional linear discriminant analysis. Advances in Neural Information Processing Systems 2004, 17:1569-1576.
16. '2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing 2005, 69(1-3):224-231.
17. Vasilescu M.A.O., Terzopoulos D. Multilinear analysis of image ensembles: tensorfaces. Proceedings of the European Conference on Computer Vision (ECCV) 2002, 447-460.
18. Tao D., Li X., Wu X., Maybank S.J. General tensor discriminant analysis and gabor features for gait recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 1700-1715.
19. Wang J., Barreto A., Wang L., Chen Y., Rishe N., Andrian J., Adjouadi M. Multilinear principal component analysis for face recognition with fewer features. Neurocomputing 2010, 73:1550-1555.
20. Martínez A.M. Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transaction on Pattern Analysis and Machine Intelligence 2002, 24(6):748-763.
21. L.V. der Maaten, E. Postma, H.V. den Herik, Dimensionality reduction: a comparative review, Technical Report, MICC, Maastricht University, 2008.
22. Phillips P.J., Moon H., Rizvi S.A., Rauss P.J. The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(10):1090-1104.
23. Chen S., Zhu Y. Subpattern-based principle component analysis. Pattern Recognition 2004, 37(5):1081-1083.
24. Achlioptas D. Database-friendly random projections. ACM Symposium on the Principles of Database Systems 2001, 274-281. ACE Press.
25. Y.S. Kim, K.-A. Toh, Sparse random projection for efficient cancelable face feature extraction, in: Proceedings of the IEEE Conference on Industrial Electronics and Applications, Singapore, June 2008, pp. 2139-2144.
26. Yan S., Xu D., Zhang B., Zhang H.-J., Yang Q., Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 40-51.
27. Zhang T., Tao D., Li X., Yang J. Patch alignment for dimensionality reduction. IEEE Transactions on Knowledge and Data Engineering 2008, 1299-1313.
28. Zhou T., Tao D., Wu X. Manifold elastic net: a unified framework for sparse dimension reduction. Data Mining and Knowledge Discovery 2010, 22(3):340-371.
29. A. Pentland, B. Moghaddam, T. Starner, View-based and modular eigenspaces for face recognition, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994. Proceedings of CVPR'94, pp. 84-91.
30. Al-Osaimi F., Bennamoun M., Mian A. Integration of local and global geometrical cues for 3D face recognition. Pattern Recognition 2008, 41(3):1030-1040.
31. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70(1-3):489-501.
32. Huang G.-B., Chen L., Siew C.-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006, 17(4):879-892.
33. Huang G.-B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70(16-18):3056-3062.
34. Huang G.-B., Zhu Q.-Y., Mao K., Siew C.-K., Saratchandran P., Sundararajan N. Can threshold networks be trained directly?. IEEE Transactions on Circuits and Systems II: Express Briefs 2006, 53(3):187-191.
35. AT&T Laboratories Cambridge, Olivetti Research Laboratory (ORL) database. (accessed June 24, 2010). http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
36. S.-K. Kim, H. Lee, S. Yu, S. LEE, Robust face recognition by fusion of visual and infrared cues, in: Proceedings of IEEE Conference on Industrial Electronics and Applications, Singapore, May 2006, pp. 804-808.
37. H. Wechsler, P. Phillips, V. Bruce, F. Fogelman-Soulie, T. Huang, Em characterizing virtual eigensignatures for general purpose face recognition, in: D.B. Graham, N.M. Allinson (Eds.), Face Recognition: From Theory to Applications, 1998, pp. 446-456.
38. '2LDA an efficient approach for face recognition. Pattern Recognition 2006, 39(7):1396-1400.
39. Jain A.K., Nandakumar K., Ross A. Score normalization in multimodal biometric systems. Pattern Recognition 2005, 38(12):2270-2285.
40. National Institute of Standards and Technology (NIST), The Facial Recognition Technology (FERET) database. (accessed June 24, 2010). http://www.itl.nist.gov/iad/humanid/feret/feretmaster.html.
41. The University of Sheffield, The Sheffield (previously UMIST) Face Database. (accessed June 24, 2010). http://www.shef.ac.uk/eee/research/iel/research/face.html.
42. The Math Works, MATLAB. http://www.mathworks.com/.