NDLI: Performance Analysis of the Feedforward and SOM Neural Networks in the Face Recognition Problem

Content Provider	IEEE Xplore Digital Library
Author	Chacon, M.I. Rivas-Perea, P.
Copyright Year	2007
Description	Author affiliation: Digital Signal Process. & Vision Lab., Chihuahua Inst. of Technol. (Chacon, M.I.; Rivas-Perea, P.)
Abstract	This paper presents a comparative study between a feedforward neural network and a SOM network. The paper also proposes the incorporation of a new spatial feature, face feature lines, FFL, to represent the faces. FFL are considered as new features based on previous studies related to face recognition tasks on newborns. Besides the face feature lines, the feature vector incorporates eigenvectors of the face image obtained with the Karhunen-Loeve transformation. A face recognition system is based on a feedforward neural network, FFBP, method. The second classification scheme uses a self organized map, SOM, architecture combined with the k-means clustering algorithm. Experiments comparing both architectures show no significant differences for the ORL database, 92% for the FFBP and 90% for the SOM. However results obtained for the Yale database, 60% for the FFBP network and 70% for the SOM, indicate a better performance with the SOM architecture
Starting Page	313
Ending Page	318
File Size	538066
Page Count	6
File Format	PDF
ISBN	1424407079
DOI	10.1109/CIISP.2007.369187
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pediatrics Face recognition Neural networks Lighting Robustness Performance analysis Feedforward neural networks Face detection Facial features Principal component analysis
Content Type	Text
Resource Type	Article

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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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