Optimized features selection for gender classification using optimization algorithms

Sajid Ali KHAN; Muhammad NAZIR; Naveed RIAZ

Article Details

Optimized features selection for gender classification using optimization algorithms

Authors:

Sajid Ali KHAN | Muhammad NAZIR | Naveed RIAZ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 5, pp.1479-1494

Document Type:

Article

Publication Date:

4/2013

Document Language:

Turkish

Document ID (Org No.):

2000743243

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

Optimized feature selection is an important task in gender classification.   The optimized features not only reduce the dimensions, but also reduce the   error rate. In this paper, we have proposed a technique for the extraction   of facial features using both appearance-based and geometric-based feature   extraction methods. The extracted features are then optimized using particle   swarm optimization (PSO) and the bee algorithm. The geometric-based features   are optimized by PSO with ensemble classifier optimization by the genetic   algorithm. Using this approach, we have obtained promising results in terms   of the classification error rate and computation time minimization.   Moreover, our optimized feature-based method is robust to illumination,   noise, and occlusion changes.

 

Optimized feature selection is an important task in gender classification.   The optimized features not only reduce the dimensions, but also reduce the   error rate. In this paper, we have proposed a technique for the extraction   of facial features using both appearance-based and geometric-based feature   extraction methods. The extracted features are then optimized using particle   swarm optimization (PSO) and the bee algorithm. The geometric-based features   are optimized by PSO with ensemble classifier optimization by the genetic   algorithm. Using this approach, we have obtained promising results in terms   of the classification error rate and computation time minimization.   Moreover, our optimized feature-based method is robust to illumination,   noise, and occlusion changes.

Article Details

Optimized features selection for gender classification using optimization algorithms

Authors: Sajid Ali KHAN | Muhammad NAZIR | Naveed RIAZ

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 5, pp.1479-1494

Document Type: Article

Publication Date: 4/2013

Document Language: Turkish

Document ID (Org No.): 2000743243

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Sajid Ali KHAN | Muhammad NAZIR | Naveed RIAZ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 5, pp.1479-1494

Document Type:

Article

Publication Date:

4/2013

Document Language:

Turkish

Document ID (Org No.):

2000743243

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632