Approximate nearest neighbor search using self-organizing map clustering for face recognition system

Approximate nearest neighbor search using self-organizing map clustering for face recognition system

© 2014 IEEE. This paper presents face recognition system that is based on Self-Organizing Map (SOM) clustering. In order to reduce the time consumption in nearest neighbor search, SOM clustering scheme is used to group the training data and determine prototypes of each group. Local feature selection...

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Main Authors: Paitoon Yodkhad, Aram Kawewong, Karn Patanukhom
Format: Conference Proceeding
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84988268343&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45257
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-452572018-01-24T06:07:27Z Approximate nearest neighbor search using self-organizing map clustering for face recognition system Paitoon Yodkhad Aram Kawewong Karn Patanukhom © 2014 IEEE. This paper presents face recognition system that is based on Self-Organizing Map (SOM) clustering. In order to reduce the time consumption in nearest neighbor search, SOM clustering scheme is used to group the training data and determine prototypes of each group. Local feature selection process is employed to reduce dimension of data in each group. To show the performance of the proposed scheme over various choices of feature extraction method, PCA (Eigenface), 2DPCA, and SOM-Face are tested in the experiment. Recognition accuracy and time consumption are measured in comparison with k-d Tree search and the other clustering based search schemes by using the dataset of 1,560 face images from 156 people. The experiments show that the proposed scheme can obtain the best recognition rate of 99.36% while it reduces the time consumption. 2018-01-24T06:07:27Z 2018-01-24T06:07:27Z 2014-01-01 Conference Proceeding 2-s2.0-84988268343 10.1109/ICSEC.2014.6978186 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84988268343&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45257
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 2014 IEEE. This paper presents face recognition system that is based on Self-Organizing Map (SOM) clustering. In order to reduce the time consumption in nearest neighbor search, SOM clustering scheme is used to group the training data and determine prototypes of each group. Local feature selection process is employed to reduce dimension of data in each group. To show the performance of the proposed scheme over various choices of feature extraction method, PCA (Eigenface), 2DPCA, and SOM-Face are tested in the experiment. Recognition accuracy and time consumption are measured in comparison with k-d Tree search and the other clustering based search schemes by using the dataset of 1,560 face images from 156 people. The experiments show that the proposed scheme can obtain the best recognition rate of 99.36% while it reduces the time consumption.
format Conference Proceeding
author Paitoon Yodkhad
Aram Kawewong
Karn Patanukhom
spellingShingle Paitoon Yodkhad
Aram Kawewong
Karn Patanukhom
Approximate nearest neighbor search using self-organizing map clustering for face recognition system
author_facet Paitoon Yodkhad
Aram Kawewong
Karn Patanukhom
author_sort Paitoon Yodkhad
title Approximate nearest neighbor search using self-organizing map clustering for face recognition system
title_short Approximate nearest neighbor search using self-organizing map clustering for face recognition system
title_full Approximate nearest neighbor search using self-organizing map clustering for face recognition system
title_fullStr Approximate nearest neighbor search using self-organizing map clustering for face recognition system
title_full_unstemmed Approximate nearest neighbor search using self-organizing map clustering for face recognition system
title_sort approximate nearest neighbor search using self-organizing map clustering for face recognition system
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84988268343&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45257
_version_ 1681422711578951680

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