NDLI: An improved adaptive discriminant analysis for single sample face recognition

Content Provider	IEEE Xplore Digital Library
Author	Wannakijmongkol, T. Khornrakhun, I. Chalidabhongse, T.H.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Eng., Chulalongkorn Univ., Bangkok, Thailand (Wannakijmongkol, T.; Khornrakhun, I.; Chalidabhongse, T.H.)
Abstract	Face recognition is an automated process with the ability to identify individuals by their facial characteristics. Currently there is a problem in which the process requires several examples of the person of interest's face in order to produce accurate outcome, and the process is intolerant to the variation in facial expression and the condition of lighting of the face image needed to be identify. This inspired us to come up with an algorithm to increase accuracy of single sample facial recognition process. In the case where multiple samples are available, the best approach to identify a person by face recognition system is to use Fischer Linear Discriminant Analysis (FLDA) method which use multiple samples to calculate the within-class scatter matrix and could give output accurately. However with only one sample it means the sample does not have any variation, hence impossible to find the within-class scatter matrix. The Adaptive Discriminant Learning (ADL) [1] was proposed to solve the problem by deducing the within-class scatter matrix from auxiliary generic set which consist of multiple samples per person then use FLDA to recognize face image. In this paper, we improve the method by preprocessing the input image using a local illumination normalization to make the feature of the face became more obvious and suppress the effect of illumination variation and incorporating a part-based methodology to further increase the recognition rate. The system was tested with the FERET face database, and the recognition rate is improved from 77% to 93%.
Sponsorship	IEEE Thailand Sect.
Starting Page	7
Ending Page	11
File Size	390913
Page Count	5
File Format	PDF
e-ISBN	9781479958221
DOI	10.1109/JCSSE.2014.6841833
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-14
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Single sample per person Adaptive discriminant analysis
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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