Abstract
This paper improves the performance of Feature Feedback and presents its application to face recognition. Feature Feedback has been introduced as a method which focuses on preprocessing the input data before classification. After extracting the features from original, Feature Feedback identifies the important part of the original data through the reverse mapping from the extracted features to the original space. In the feature extraction step, original feature feedback used PCA before LDA to avoid the small sample size problem but it has been shown that this may cause loss of significant discriminatory information. To overcome that problem, in the proposed method, we introduce feature feedback using regularized Fisher’s separability criterion to extract the features and apply it to face recognition using the Yale data. The experimental results show that the proposed method works well.
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References
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neurosci. 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Yu, H., Yang, J.: A direct LDA algorithm for high dimensional data – with application to face recognition. Pattern Recognition 34, 2067–2070 (2001)
Chen, L.F., Liao, H.Y.M., Ko, M.T., Lin, J.C., Yu, G.J.: A new LDA- based face recognition system which can solve the small size sample. Pattern Recognition 33, 1713–1726 (2000)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using LDA Based Algorithms. IEEE Transactions on Neural Networks 14(1), 195–200 (2003)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Regularization Studies of Linear Discriminant Analysis in Small Sample Size Scenarios with Application to Face Recognition. Pattern Recognition Letter 26, 181–191 (2005)
Maio, D., Maltoni, D.: A structural approach to fingerprint classification. In: IEEE Int’l Conf. Image Process. (1996)
Yang, Y., Ha, S., Kim, Y.: A matched – profile method for simple and robust vapor recognition in electronic nose (E-nose) system. Sensors and Actuators B 106, 263–270 (2005)
Althaniz, P., Goschnick, J., Ehrmann, S., Ache, H.J.: Multisensor, Microsystem for contaminants in air. In: Int’l Conf. Solid – State Sensors and Actuators (1996)
Fukunaga, K.: Introduction to Statistical pattern Recognition, 2nd edn. Academic Press, London (1990)
Pang, Y., Zhang, L., Li, M., Liu, Z., Ma, W.: A novel Gabor – LDA based face recognition method. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3331, pp. 352–358. Springer, Heidelberg (2004)
Kokipoulou, E., Frossard, P.: Classification-specific feature sampling for face recognition. In: IEEE Workshop on Multimedia Signal Processing, pp. 20–23 (2006)
Choi, S.-I., Choi, C.-H., Jeong, G.-M.: Pixel selection in a face image based on discriminant features for face recognition. In: IEEE Int’l Conf. Automatic Face and Gesture Recognition (2008)
Jeong, G.-M., Ahn, H.-S., Choi, S.-I., Kwak, N.-J., Moon, C.: Pattern Recognition using Feature Feedback: Application to Face Recognition. Internaltional Journal of Control, Automation, and Systems 8, 141–148 (2010)
Choi, S.-I., Kim, S.-H., Yang, Y.-S., Jeong, G.-M.: Data Refinement and Channel Selection for a Portable E-Nose System by the Use of Feature Feedback. Sensors, 10387–10400 (2010)
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Truong, L.B., Choi, SI., Jeong, GM., Seo, JM. (2011). An Improvement in Feature Feedback Using R-LDA with Application to Yale Database. In: Lee, G., Howard, D., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Lecture Notes in Computer Science, vol 6935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24082-9_43
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DOI: https://doi.org/10.1007/978-3-642-24082-9_43
Publisher Name: Springer, Berlin, Heidelberg
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