Cohort normalization based sparse representation for undersampled face recognition
Sun, Yunlian, Fookes, Clinton B., Poh, Norman, & Tistarelli, Massimo (2013) Cohort normalization based sparse representation for undersampled face recognition. In Lee, K.M, Matsushita , Y, Rehg, J.M., & Hu, Z (Eds.) Proceedings of the 11th Asian Conference on Computer Vision (ACCV) Workshop, Lecture Notes in Computer Science vol. 7724, Springer, Korea.
Abstract. In recent years, sparse representation based classification(SRC) has received much attention in face recognition with multipletraining samples of each subject. However, it cannot be easily applied toa recognition task with insufficient training samples under uncontrolledenvironments. On the other hand, cohort normalization, as a way of mea-suring the degradation effect under challenging environments in relationto a pool of cohort samples, has been widely used in the area of biometricauthentication. In this paper, for the first time, we introduce cohort nor-malization to SRC-based face recognition with insufficient training sam-ples. Specifically, a user-specific cohort set is selected to normalize theraw residual, which is obtained from comparing the test sample with itssparse representations corresponding to the gallery subject, using poly-nomial regression. Experimental results on AR and FERET databases show that cohort normalization can bring SRC much robustness against various forms of degradation factors for undersampled face recognition.
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|Item Type:||Conference Paper|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 Springer|
|Deposited On:||22 Mar 2013 00:24|
|Last Modified:||08 May 2014 12:25|
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