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.

View at publisher


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.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

48 since deposited on 22 Mar 2013
16 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 58516
Item Type: Conference Paper
Refereed: Yes
ISBN: 978-3-642-37330-5
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page