NDLI: Face recognition using modular Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Sharma, D. Dhar, J.
Copyright Year	2010
Description	Author affiliation: Department of Information Communication and Technology, ABV - Indian Institute of Information Technology and Management Gwalior, India (Sharma, D.; Dhar, J.)
Abstract	Monolithic Neural Networks are generally prone to sub-optimal performance in highly complex and dimensional problems that hinders learning. Modular Neural Networks employ a divide and conquer strategy to convert a complex problem into a set of simpler problems. In classification this means focus upon local features and making of simpler feature space. The simpler problems in a modular architecture are solved by different modules or experts, each of whose outputs are integrated to give the final output. Each module is a combination of feature extraction technique and classifier, and returns a matching score as output. The paper presents a two step modular architecture. At the first step the facial image is decomposed into 3 sub-images. At the second stage each sub-image is solved redundantly by two different neural network models and features extraction techniques. Two step integration is performed using probabilistic sum, min, max, product and polling integration techniques. The proposed modular architecture gives improvised matching score with all integration techniques.
File Size	741696
File Format	PDF
ISBN	9781424486670
e-ISBN	9781424486663
DOI	10.1109/ICSTE.2010.5608884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Neurons Back Propagation (BP) Artificial neural networks Computer architecture Regularized-Linear Discriminant Analysis (R-LDA) Radial Basis Function (RBF) Feature extraction Principal Component Analysis (PCA) Biological neural networks Face Recognition Principal component analysis
Content Type	Text
Resource Type	Article

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