NDLI: Face class code based feature extraction for face recognition

Content Provider	IEEE Xplore Digital Library
Author	Xie, C. Vijaya Kumar, B.V.K.
Copyright Year	2005
Description	Author affiliation: Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA (Xie, C.; Vijaya Kumar, B.V.K.)
Abstract	In face recognition, the goal is to assign a class label for a test image of a subject from N classes in the database, when binary classifiers are used, the commonly used method is the one-per-class (OPC) i.e., one classifier per subject. A drawback of the OPC method is that when the number of classes is large, it takes very long time to make a classification decision. In place of the computationally-demanding OPC method, we propose a new feature extraction method "face class code" (FCC) based on binary classifiers. For example, correlation filters and support vector machines can be used to generate feature vectors to deal with large number of classes. The FCC method encodes each class label into a binary string, and we design classifiers to discriminate '1' or '0' for each bit in the sequence, to determine the class label. Thus, we will need as few as [log/sub 2/(N)] binary classifiers to achieve an N-class recognition problem. This binary coding framework also opens the whole world of error control codes (ECC), which can be used to improve the recognition performance. The proposed method is verified through experiments on the PIE database and the AR database.
Sponsorship	CUBS (Center for Unified Biometrics and Sensors) Ultra-Scan CUBRC
Starting Page	257
Ending Page	262
File Size	205866
Page Count	6
File Format	PDF
ISBN	0769524753
DOI	10.1109/AUTOID.2005.22
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Face recognition FCC Testing Image databases Spatial databases Filters Support vector machines Support vector machine classification Error correction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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