Gender recognition on real world faces based on shape representation and neural network
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 e...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/39302/ http://psasir.upm.edu.my/id/eprint/39302/1/Gender%20recognition%20on%20real%20world%20faces%20based%20on%20shape%20representation%20and%20neural%20network.pdf |
| _version_ | 1848849108417118208 |
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| author | Arigbabu, Olasimbo Ayodeji Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Iranmanesh, Vahab Malallah, Fahad Layth |
| author_facet | Arigbabu, Olasimbo Ayodeji Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Iranmanesh, Vahab Malallah, Fahad Layth |
| author_sort | Arigbabu, Olasimbo Ayodeji |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T09:45:09Z |
| format | Conference or Workshop Item |
| id | upm-39302 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:45:09Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-393022016-07-29T01:16:49Z http://psasir.upm.edu.my/id/eprint/39302/ Gender recognition on real world faces based on shape representation and neural network Arigbabu, Olasimbo Ayodeji Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Iranmanesh, Vahab Malallah, Fahad Layth 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. IEEE 2014 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/39302/1/Gender%20recognition%20on%20real%20world%20faces%20based%20on%20shape%20representation%20and%20neural%20network.pdf 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. . 10.1109/ICCOINS.2014.6868361 |
| spellingShingle | Arigbabu, Olasimbo Ayodeji Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Iranmanesh, Vahab Malallah, Fahad Layth Gender recognition on real world faces based on shape representation and neural network |
| title | Gender recognition on real world faces based on shape representation and neural network |
| title_full | Gender recognition on real world faces based on shape representation and neural network |
| title_fullStr | Gender recognition on real world faces based on shape representation and neural network |
| title_full_unstemmed | Gender recognition on real world faces based on shape representation and neural network |
| title_short | Gender recognition on real world faces based on shape representation and neural network |
| title_sort | gender recognition on real world faces based on shape representation and neural network |
| url | http://psasir.upm.edu.my/id/eprint/39302/ http://psasir.upm.edu.my/id/eprint/39302/ http://psasir.upm.edu.my/id/eprint/39302/1/Gender%20recognition%20on%20real%20world%20faces%20based%20on%20shape%20representation%20and%20neural%20network.pdf |