An Improvement in Feature Feedback Using R-LDA with Application to Yale Database
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.
KeywordsFeature Feedback Face Recognition Feature Mask
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- 8.Maio, D., Maltoni, D.: A structural approach to fingerprint classification. In: IEEE Int’l Conf. Image Process. (1996)Google Scholar
- 10.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)Google Scholar
- 13.Kokipoulou, E., Frossard, P.: Classification-specific feature sampling for face recognition. In: IEEE Workshop on Multimedia Signal Processing, pp. 20–23 (2006)Google Scholar
- 14.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)Google Scholar
- 16.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)Google Scholar