Comparison of several classification algorithms for gender recognition from face images

Mutlu Sakarkaya; Fahrettin Yanbol; Zeyneb Kurt

doi:10.1109/INES.2012.6249810

Comparison of several classification algorithms for gender recognition from face images

Mutlu Sakarkaya^*, Fahrettin Yanbol, Zeyneb Kurt

^*Corresponding author for this work

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

14 Citations (Scopus)

Abstract

This paper presents a comparison between several algorithms which were employed for gender recognition automatically. Firstly, the face images of various mature women and men samples were gathered, and face images were separated as train dataset and test dataset. Both of the datasets were pre-processed and made ready for following operations. Secondly, Principal Component Analysis (PCA) was applied to train dataset to extract the most distinguishing features. Finally, three classification algorithms, Support Vector Machine (SVM), k-Nearest Neighbourhood (k-NN), and Multivariate Classification with Multivariate Gauss Distribution (MCMGD) algorithms were implemented and compared to determine the most suitable and successful algorithm for gender recognition from face images. Experimental results illustrate that k-NN with k values 5, 7, 9 outperformed the other approaches.

Original language	English
Title of host publication	INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings
Pages	97-101
Number of pages	5
DOIs	https://doi.org/10.1109/INES.2012.6249810
Publication status	Published - 1 Oct 2012
Externally published	Yes
Event	IEEE 16th International Conference on Intelligent Engineering Systems, INES 2012 - Lisbon, Portugal Duration: 13 Jun 2012 → 15 Jun 2012

Publication series

Name	INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings

Conference

Conference	IEEE 16th International Conference on Intelligent Engineering Systems, INES 2012
Country/Territory	Portugal
City	Lisbon
Period	13/06/12 → 15/06/12

Access to Document

10.1109/INES.2012.6249810

Cite this

Sakarkaya, M., Yanbol, F., & Kurt, Z. (2012). Comparison of several classification algorithms for gender recognition from face images. In INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings (pp. 97-101). Article 6249810 (INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings). https://doi.org/10.1109/INES.2012.6249810

@inproceedings{906f241195374907a5eaabdf7fb08961,

title = "Comparison of several classification algorithms for gender recognition from face images",

abstract = "This paper presents a comparison between several algorithms which were employed for gender recognition automatically. Firstly, the face images of various mature women and men samples were gathered, and face images were separated as train dataset and test dataset. Both of the datasets were pre-processed and made ready for following operations. Secondly, Principal Component Analysis (PCA) was applied to train dataset to extract the most distinguishing features. Finally, three classification algorithms, Support Vector Machine (SVM), k-Nearest Neighbourhood (k-NN), and Multivariate Classification with Multivariate Gauss Distribution (MCMGD) algorithms were implemented and compared to determine the most suitable and successful algorithm for gender recognition from face images. Experimental results illustrate that k-NN with k values 5, 7, 9 outperformed the other approaches.",

author = "Mutlu Sakarkaya and Fahrettin Yanbol and Zeyneb Kurt",

year = "2012",

month = oct,

day = "1",

doi = "10.1109/INES.2012.6249810",

language = "English",

isbn = "9781467326957",

series = "INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings",

pages = "97--101",

booktitle = "INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings",

note = "IEEE 16th International Conference on Intelligent Engineering Systems, INES 2012 ; Conference date: 13-06-2012 Through 15-06-2012",

}

Sakarkaya, M, Yanbol, F & Kurt, Z 2012, Comparison of several classification algorithms for gender recognition from face images. in INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings., 6249810, INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings, pp. 97-101, IEEE 16th International Conference on Intelligent Engineering Systems, INES 2012, Lisbon, Portugal, 13/06/12. https://doi.org/10.1109/INES.2012.6249810

Comparison of several classification algorithms for gender recognition from face images. / Sakarkaya, Mutlu; Yanbol, Fahrettin; Kurt, Zeyneb.
INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings. 2012. p. 97-101 6249810 (INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings).

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

TY - GEN

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AU - Yanbol, Fahrettin

AU - Kurt, Zeyneb

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N2 - This paper presents a comparison between several algorithms which were employed for gender recognition automatically. Firstly, the face images of various mature women and men samples were gathered, and face images were separated as train dataset and test dataset. Both of the datasets were pre-processed and made ready for following operations. Secondly, Principal Component Analysis (PCA) was applied to train dataset to extract the most distinguishing features. Finally, three classification algorithms, Support Vector Machine (SVM), k-Nearest Neighbourhood (k-NN), and Multivariate Classification with Multivariate Gauss Distribution (MCMGD) algorithms were implemented and compared to determine the most suitable and successful algorithm for gender recognition from face images. Experimental results illustrate that k-NN with k values 5, 7, 9 outperformed the other approaches.

AB - This paper presents a comparison between several algorithms which were employed for gender recognition automatically. Firstly, the face images of various mature women and men samples were gathered, and face images were separated as train dataset and test dataset. Both of the datasets were pre-processed and made ready for following operations. Secondly, Principal Component Analysis (PCA) was applied to train dataset to extract the most distinguishing features. Finally, three classification algorithms, Support Vector Machine (SVM), k-Nearest Neighbourhood (k-NN), and Multivariate Classification with Multivariate Gauss Distribution (MCMGD) algorithms were implemented and compared to determine the most suitable and successful algorithm for gender recognition from face images. Experimental results illustrate that k-NN with k values 5, 7, 9 outperformed the other approaches.

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DO - 10.1109/INES.2012.6249810

M3 - Conference contribution

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SN - 9781467326957

T3 - INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings

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BT - INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings

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Sakarkaya M, Yanbol F, Kurt Z. Comparison of several classification algorithms for gender recognition from face images. In INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings. 2012. p. 97-101. 6249810. (INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings). doi: 10.1109/INES.2012.6249810

Comparison of several classification algorithms for gender recognition from face images

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