Face recognition using self-organizing feature maps and support vector machines

Proceedings - 5th International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003

Volumn , Issue , 2003, Pages 37-42

  Li, Chaoyang ^a   Liu, Fang ^a   Xie, Yinxiang ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Computational intelligence; Face recognition; Support vector machines

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; CONFORMAL MAPPING; SELF ORGANIZING MAPS; SUPPORT VECTOR MACHINES;

CORTICAL STRUCTURES; FACE DATABASE; GENERALIZATION CAPABILITY; RECOGNITION ABILITIES; SUPPORT VECTOR MACHINE (SVMS); VISUAL ENVIRONMENTS;

FACE RECOGNITION;

EID: 16644392326     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCIMA.2003.1238097     Document Type: Conference Paper

References (17)

1. Turk M, Pentland A "Eigenfaces for recognition,"[J].J Cognitive Neuroscience,1991;3(1):71-86
2. Anastasios Tefas, Constantine Kotropoulos,loannis pitas. "Using Support Vector Machines to Enhance the performce of Elastic Graph Matching for Frontal Face Authentiction,"IEEE Trans. Pattern Analysis and Machine Intelligence,vol.23,no.7,pp.735-746,July.2001
3. Lades, J Vorbuggen, J Buhmann et al."Distortion invariant object recognition in the dynamic link architecture,"[J].IEEE Trans. On Computers,1993;42(3),pp.300-311
4. G.A.KHUWAJA."A Parameter-based Combined Classifier for invariant facial expression and gender Recognition," Pattern Recognition and Artificial Intelligence, vol.16,no.1,pp.27-51,February,2002.
5. James.A. Bednar, Risto Miikkulainen "Self-Organizing of Innate Face Preference:Could Genetice Be expressed Through Learning?," proceeding of the seventeenth National conference on Artificial Intelligence(AAAI-2000),2000,Cambridge,MA:MIT press,pp. 117-122.
6. MU-CHUN SU, HSIAO-TE CHANG, CHIEN-HSING CHOU. A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps. Neural Processing Letters. 15:137-145,2002.
7. Chengjun Liu, Harry Wechsler. Evolutionary Pursuit and its Application to Face Recognition. IEEE Trans. Pattern Analysis and Machine Intelligeng, vol.22,no.6,pp.570-581,2000.
8. V.Vapnik. Statistical Learning Theory. John Wiley and Sons, New York,1998.
9. D.Michie, D.J.Spiegelhalter,C.C.Taylor.(1994) Machine Learning, Neural and Statistical Classification [online]. Available: ftp.ncc.up.pt/pub/statlog/.
10. O.Chapelle,V.Vapnik, O.Bousquet, S.Mukherjee. Chosing kernel patameters for support vector machines. Machine Learning,2000.
11. A.Blum, P.Langley. Selection of relevant feature and examples in machine learning. Artificial Intelligence.97:245-271,1997.
12. Ronan Collobert. A Parallel Mixture of SVMs for very Large Scale Problems. Neural Computtation 14,1105-1114(2002).
13. Chin-Wei HSU, Chih-Jen Lin. A Comparison of Methods for Multiclass Support Vector Machines. IEEE Trans. Neural Networks, vol.13,no.2,pp.415-425,MARCH 2002.
14. AT&T Laboratories, Cambridge, http://www.cam-orl.co.uk/facedatabase.html.
15. Wiskott, C.von cler Malsburg, Recognizing faces by dynamic link matching. Neuroimage 4(1996) pp. 14-18.
16. J.Weston, S.Mukherjee, O.Chapeiie and M.Pontil. Feature Selection for SVMs. Heep:\\www.svm\application.
17. UMIST database http://images.ee.uniist.ac.uk/danny/database.html.