NDLI: A comparative survey on supervised classifiers for face recognition

Content Provider	IEEE Xplore Digital Library
Author	Arriaga-Gomez, M.F. de Mendizabal-Vazquez, I. Ros-Gomez, R. Sanchez-Avila, C.
Copyright Year	2014
Description	Author affiliation: ETS Ing. de Telecomun., Univ. Politec. de Madrid, Madrid, Spain (Ros-Gomez, R.) \|\| Group of Biometrics, Biosignals & Security, Univ. Politec. de Madrid, Pozuelo de Alarcón, Spain (Arriaga-Gomez, M.F.; de Mendizabal-Vazquez, I.; Sanchez-Avila, C.)
Abstract	During the last decades, several different techniques have been proposed for computer recognition of human faces. A further step in the development of these biometrics is to implement them in portable devices, such as mobile phones. Due to this devices' features and limitations it is necessary to select, among the currently available algorithms, the one with the best performance in terms of algorithm overall elapsed time and correct identification rates. The aim of this paper is to offer a complementary study to previous works, focusing on the performance of different supervised classifiers, such as the Normal Bayesian Classifier, Neural Architectures or distance-based algorithms. In addition, we analyse all the proposed algorithms' efficiency over public face databases (ORL, FERET, NIST and the Face Recognition Data from the Essex University). Each one of these databases contains a different number of individuals and particular samples and they present variations among images from the same user (scale, pose, expression, illumination, ...). We expect to simulate many different situations which take place when dealing with face recognition on mobile phones. In order to get a complete comparison, all the proposed algorithms have been implemented and run over all the databases, using the same computer. Different parametrizations for each algorithm have also been tested. Bayesian classifiers and distance-based algorithms turn out to be the most suitable, as their parametrization is simple, the training stage is not as time consuming as others' and classification results are satisfying.
Starting Page	1
Ending Page	6
File Size	284481
Page Count	6
File Format	PDF
ISBN	9781479935307
ISSN	21530742
e-ISBN	9781479935321
DOI	10.1109/CCST.2014.6987036
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-13
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Databases Training Feature extraction Image color analysis NIST Vectors LDA Biometrics face recognition supervised classifiers machine learning PCA
Content Type	Text
Resource Type	Article

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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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