DSpace@nitr: Item 2080/1703

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1703

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DC Field	Value	Language
contributor.author	Kumari, S	-
contributor.author	Bakshi, S	-
contributor.author	Majhi, B	-
date.accessioned	2012-05-17T04:13:28Z	-
date.available	2012-05-17T04:13:28Z	-
date.issued	2012-03	-
identifier.citation	3rd National Conference on Emerging Trends and Applications in Computer Science (NCETACS) 30-31 March 2012	en
identifier.uri	http://hdl.handle.net/2080/1703	-
description	Copyright for this paper belongs to IEEE	en
description.abstract	The research presented in this paper proposes a novel gender classification approach using face image. The approach extracts features from grayscale face images through Infomax ICA and subsequently selects features using k-means clustering and classifies the clustered features employing PNN. All the experimental evaluations are done on cropped face images from FERET database using 280 faces for training and 120 different faces for testing. The approach, when features are not clustered gives maximum accuracy of 93.33%. However the proposed approach yields 95% accuracy through employing clustering on features, which is significant for gender classification using low resolution (118 × 97) face images.	en
format.extent	592516 bytes	-
format.mimetype	application/pdf	-
language.iso	en	-
subject	gender classification	en
subject	PNN	en
subject	K-mean clusterings	en
subject	ICA	en
title	Improving Performance of PNN using Clustered ICs for Gender Classification	en
type	Article	en
Appears in Collections:	Conference Papers

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