Cost-sensitive face recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 32, Issue 10, 2010, Pages 1758-1769

  Zhang, Yin ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

cost sensitive face recognition; cost sensitive learning; Face recognition; multiclass cost sensitive learning

Indexed keywords

APPLICATION SCENARIO; COST-SENSITIVE; COST-SENSITIVE FACE RECOGNITION; COST-SENSITIVE LEARNING; FACE RECOGNITION SYSTEMS; MISCLASSIFICATIONS; MULTI-CLASS; RECOGNITION ERROR;

COSTS;

FACE RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOMETRY; FACE; FACTUAL DATABASE; HISTOLOGY; HUMAN; IMAGE PROCESSING; METHODOLOGY; STATISTICAL ANALYSIS;

ALGORITHMS; BIOMETRIC IDENTIFICATION; DATA INTERPRETATION, STATISTICAL; DATABASES, FACTUAL; FACE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER);

EID: 77956060133     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2009.195     Document Type: Article

References (27)

1. N. Abe, B. Zadrozny, and J. Langford, "An Iterative Method for Multi-Class Cost-Sensitive Learning," Proc. ACM SIGKDD, pp. 311, 2004.
2. T. Ahonen, A. Hadid, and M. Pietikainen, "Face Description with Local Binary Patterns," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.28, no.12, pp. 2037-2041, Dec. 2006.
3. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, "Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.26, no.4, pp. 711-720, July 1997.
4. M. Ciraco, M. Rogalewski, and G.M. Weiss, "Improving Classifier Utility by Altering the Misclassification Cost Ratio," Proc. Knowledge Discovery and Data Mining Workshop Utility-Based Data Mining, pp. 46-52, 2005.
5. P. Domingos, "MetaCost: A General Method for Making Classifiers Cost-Sensitive," Proc. ACM SIGKDD, pp. 155-164,1999.
6. C. Drummond and R.C. Holte, "C4.5 and Class Imbalance and Cost Sensitivity: Why Under Sampling Beats Over-Sampling," Proc. Working Notes Int'l Conf. Machine Learning Workshop Learning from Imbalanced Data Sets, 2003.
7. C. Elkan, "The Foundations of Cost-Sensitive Learning," Proc. 17th Int'l Joint Conf. Artificial Intelligence, pp. 973-978, 2001.
8. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer, 2001.
9. X. He, S. Yan, Y. Hu, P. Niyogi, and H.J. Zhang, "Face Recognition Using Laplacian Faces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.27, no.3, pp. 328-340, Mar. 2005.
10. B. Heisele, P. Ho, and T. Poggio, "Face Recognition with Support Vector Machines: Global versus Component-Based Approach," Proc. Eighth IEEE Int'l Conf. Computer Vision, vol.2, pp. 688-694, 2001.
11. A.K. Jain, A. Ross, and S. Prabhakar, "An Introduction to Biometric Recognition," IEEE Trans. Circuits and Systems for Video Technology, vol.14, no.1, pp. 4-20, Jan. 2004.
12. T.C. Landgrebe and R.P. Duin, "Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.30, no.5, pp. 810-822, May 2008.
13. Y. Lee, Y. Lin, and G. Wahba, "Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data," J. Am. Statistical Assoc., vol.99, no.465, pp. 67-81, 2004.
14. F. Li and H. Wechsler, "Open Set Face Recognition Using Transduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.27, no.11, pp. 1686-1697, Nov. 2005.
15. Z. Li and X. Tang, "Bayesian Face Recognition Using Support Vector Machine and Face Clustering," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol.2, pp. 374-380, 2004.
16. X.-Y. Liu and Z.-H. Zhou, "The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study," Proc. Sixth IEEE Int'l Conf. Data Mining, pp. 970-974, 2006.
17. M.A. Maloof, "Learning When Data Sets Are Imbalanced and When Costs Are Unequal and Unknown," Proc. Working Notes of the Int'l Conf. Machine Learning Workshop Learning from Imbalanced Data Sets, 2003.
18. A.M. Martinez and R. Benavente, "The AR Face Database," Technical Report 24, CVC, 1998.
19. B. Moghaddam and A. Pentland, "Probabilistic Visual Learning for Object Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.19, no.7, pp. 696-710, July 1997.
20. P. Phillips, H. Moon, S. Rizvi, and P. Rauss, "The FERET Evaluation Methodology for Face-Recognition Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, no.10, pp. 1090-1104, Oct. 2000.
21. K.M. Ting, "An Empirical Study of MetaCost Using Boosting Algorithms," Proc. 11th European Conf. Machine Learning, pp. 413425, 2000.
22. K.M. Ting, "An Instance-Weighting Method to Induce Cost- Sensitive Trees," IEEE Trans. Knowledge and Data Eng., vol.14, no.3, pp. 659-665, May/June 2002.
23. M.A. Turk and A. Pentland, "Eigenface for Recognition," J. Cognitive Neuroscience, vol.3, no.1, pp. 71-86, 1991.
24. Y. Zhang and Z.-H. Zhou, "Cost-Sensitive Face Recognition," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2008.
25. W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol.35, no.1, pp. 399-458, 2003.
26. Z.-H. Zhou and X.-Y. Liu, "On Multi-Class Cost-Sensitive Learning," Proc. 21st Nat'l Conf. Artificial Intelligence, pp. 567-572, 2006.
27. J. Zhu and T. Hastie, "Classification of Gene Microarrays by Penalized Logistic Regression," Biostatistics, vol.5, no.3, pp. 427443, 2004.