NDLI: Two-view face recognition using Bayesian fusion

Content Provider	IEEE Xplore Digital Library
Author	Tsai, G.S.-Y. Tang, A.C.-W.
Copyright Year	2009
Description	Author affiliation: Department of Electrical Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan (Tsai, G.S.-Y.) \|\| Tang Department of Communication Engineering, National Central University Jhongli City, Taoyuan County 32001, Taiwan (Tang, A.C.-W.)
Abstract	Two-dimensional face recognition suffered from pose changes, while three-dimensional approaches are with high computational complexity. Motivated by this, a two-view face recognition system for digital home is presented in this paper. Besides the improvement in recognition rate, this system reduces the misclassification that could occur in traditional single-view systems. The proposed system fuses the individual recognition results of two images of the same identity with different viewing angles based on Bayesian theory. Bayesian approach uses the similarity of each person and is trained by determining the reliability of each identity of the two channels. A frontal view and a side view are chosen since they convey the most important information of human faces. Each input image is sent into its corresponding channel to obtain a 2D face recognition result. Within each channel, PCA and SVM are applied. Different form traditional PCA based approaches, SVM classifiers are used instead of minimum distance classifier to enhance the robustness. Our experimental results show that this two-view face recognition system has achieved a higher recognition rate compared with traditional 2D single-view face recognition systems.
Starting Page	157
Ending Page	162
File Size	259218
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346579
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Bayesian methods Principal component analysis Support vector machines Support vector machine classification Computational complexity Fuses Image recognition Humans Robustness multi-channel PCA SVM Bayesian theory fusion face recognition
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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