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Gender recognition on real world faces based on shape representation and neural network


Citation

Arigbabu, Olasimbo Ayodeji and Syed Ahmad, Sharifah Mumtazah and Wan Adnan, Wan Azizun and Yussof, Salman and Iranmanesh, Vahab and Malallah, Fahad Layth (2014) Gender recognition on real world faces based on shape representation and neural network. In: 2014 International Conference on Computer and Information Sciences (ICCOINS 2014), 3-5 June 2014, Kuala Lumpur, Malaysia. .

Abstract / Synopsis

Gender as a soft biometric attribute has been extensively investigated in the domain of computer vision because of its numerous potential application areas. However, studies have shown that gender recognition performance can be hindered by improper alignment of facial images. As a result, previous experiments have adopted face alignment as an important stage in the recognition process, before performing feature extraction. In this paper, the problem of recognizing human gender from unaligned real world faces using single image per individual is investigated. The use of feature descriptor to form shape representation of face images with any arbitrary orientation from the cropped version of Labeled Faces in the Wild (LFW) dataset is proposed. By combining the feature extraction technique with artificial neural network for classification, a recognition rate of 89.3% is attained.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1109/ICCOINS.2014.6868361
Publisher: IEEE
Keywords: Gender recognition; Shape representation; Neural network
Depositing User: Nursyafinaz Mohd Noh
Date Deposited: 23 Jul 2015 00:44
Last Modified: 29 Jul 2016 01:16
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICCOINS.2014.6868361
URI: http://psasir.upm.edu.my/id/eprint/39302
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