Fusion of multiple texture representations for palmprint recognition using neural networks

Galal M. Binmakhashen; El Sayed M. El-Alfy

doi:10.1007/978-3-642-34500-5_49

Fusion of multiple texture representations for palmprint recognition using neural networks

Galal M. Binmakhashen^*, El Sayed M. El-Alfy

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

During the last decade, palmprint recognition has received an increasing attention due to the abundant features that can be extracted from the captured palmprint image. However, a single palmprint texture representation may not be sufficient for reliable recognition. Therefore, in this paper we propose a computational model for palmprint recognition/identification by fusing different categories of feature-level representations using a multilayer perceptron (MLP) neural network. Features are extracted using Gabor filters and Principle Component Analysis (PCA) is used to reduce the dimensionality of the feature space by selecting the most relevant features for recognition. The proposed model has shown promising results in comparison with naïve Bayes, rule based and K*algorithm.

Original language	English
Title of host publication	Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages	410-417
Number of pages	8
Edition	PART 5
DOIs	https://doi.org/10.1007/978-3-642-34500-5_49
State	Published - 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 5
Volume	7667 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Biometrics
Gabor Filter
Machine Learning
Multilayer Perceptron
Palmprint
Pattern Recognition
Principle Component Analysis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-34500-5_49

Cite this

Binmakhashen, G. M., & El-Alfy, E. S. M. (2012). Fusion of multiple texture representations for palmprint recognition using neural networks. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings (PART 5 ed., pp. 410-417). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7667 LNCS, No. PART 5). https://doi.org/10.1007/978-3-642-34500-5_49

Binmakhashen, Galal M. ; El-Alfy, El Sayed M. / Fusion of multiple texture representations for palmprint recognition using neural networks. Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 5. ed. 2012. pp. 410-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 5).

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title = "Fusion of multiple texture representations for palmprint recognition using neural networks",

abstract = "During the last decade, palmprint recognition has received an increasing attention due to the abundant features that can be extracted from the captured palmprint image. However, a single palmprint texture representation may not be sufficient for reliable recognition. Therefore, in this paper we propose a computational model for palmprint recognition/identification by fusing different categories of feature-level representations using a multilayer perceptron (MLP) neural network. Features are extracted using Gabor filters and Principle Component Analysis (PCA) is used to reduce the dimensionality of the feature space by selecting the most relevant features for recognition. The proposed model has shown promising results in comparison with na{\"i}ve Bayes, rule based and K*algorithm.",

keywords = "Biometrics, Gabor Filter, Machine Learning, Multilayer Perceptron, Palmprint, Pattern Recognition, Principle Component Analysis",

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Binmakhashen, GM & El-Alfy, ESM 2012, Fusion of multiple texture representations for palmprint recognition using neural networks. in Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 5 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 5, vol. 7667 LNCS, pp. 410-417. https://doi.org/10.1007/978-3-642-34500-5_49

Fusion of multiple texture representations for palmprint recognition using neural networks. / Binmakhashen, Galal M.; El-Alfy, El Sayed M.
Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 5. ed. 2012. p. 410-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7667 LNCS, No. PART 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - During the last decade, palmprint recognition has received an increasing attention due to the abundant features that can be extracted from the captured palmprint image. However, a single palmprint texture representation may not be sufficient for reliable recognition. Therefore, in this paper we propose a computational model for palmprint recognition/identification by fusing different categories of feature-level representations using a multilayer perceptron (MLP) neural network. Features are extracted using Gabor filters and Principle Component Analysis (PCA) is used to reduce the dimensionality of the feature space by selecting the most relevant features for recognition. The proposed model has shown promising results in comparison with naïve Bayes, rule based and K*algorithm.

AB - During the last decade, palmprint recognition has received an increasing attention due to the abundant features that can be extracted from the captured palmprint image. However, a single palmprint texture representation may not be sufficient for reliable recognition. Therefore, in this paper we propose a computational model for palmprint recognition/identification by fusing different categories of feature-level representations using a multilayer perceptron (MLP) neural network. Features are extracted using Gabor filters and Principle Component Analysis (PCA) is used to reduce the dimensionality of the feature space by selecting the most relevant features for recognition. The proposed model has shown promising results in comparison with naïve Bayes, rule based and K*algorithm.

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KW - Gabor Filter

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KW - Pattern Recognition

KW - Principle Component Analysis

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Binmakhashen GM , El-Alfy ESM. Fusion of multiple texture representations for palmprint recognition using neural networks. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 5 ed. 2012. p. 410-417. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 5). doi: 10.1007/978-3-642-34500-5_49

Fusion of multiple texture representations for palmprint recognition using neural networks

Abstract

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