Soft biometrics: gender recognition from unconstrained face images using local feature descriptor
Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple sa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/34788/ http://psasir.upm.edu.my/id/eprint/34788/1/Soft%20biometrics%20gender%20recognition%20from%20unconstrained%20face%20images%20using%20local%20feature%20descriptor.pdf |
| _version_ | 1848847871781109760 |
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| author | Arigbabu, Olasimbo Ayodeji Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Mahmood, Saif |
| author_facet | Arigbabu, Olasimbo Ayodeji Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Mahmood, Saif |
| author_sort | Arigbabu, Olasimbo Ayodeji |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifier, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images. |
| first_indexed | 2025-11-15T09:25:30Z |
| format | Article |
| id | upm-34788 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:25:30Z |
| publishDate | 2015 |
| publisher | Universiti Utara Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-347882016-10-10T04:18:57Z http://psasir.upm.edu.my/id/eprint/34788/ Soft biometrics: gender recognition from unconstrained face images using local feature descriptor Arigbabu, Olasimbo Ayodeji Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Mahmood, Saif Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifier, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images. Universiti Utara Malaysia Press 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/34788/1/Soft%20biometrics%20gender%20recognition%20from%20unconstrained%20face%20images%20using%20local%20feature%20descriptor.pdf Arigbabu, Olasimbo Ayodeji and Syed Ahmad Abdul Rahman, Sharifah Mumtazah and Wan Adnan, Wan Azizun and Yussof, Salman and Mahmood, Saif (2015) Soft biometrics: gender recognition from unconstrained face images using local feature descriptor. Journal of Information and Communication Technology, 14. pp. 111-122. ISSN 1675-414X; ESSN: 2180-3862 http://www.jict.uum.edu.my/index.php/previous-issues/143-vol-14-2015 |
| spellingShingle | Arigbabu, Olasimbo Ayodeji Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Mahmood, Saif Soft biometrics: gender recognition from unconstrained face images using local feature descriptor |
| title | Soft biometrics: gender recognition from unconstrained face images using local feature descriptor |
| title_full | Soft biometrics: gender recognition from unconstrained face images using local feature descriptor |
| title_fullStr | Soft biometrics: gender recognition from unconstrained face images using local feature descriptor |
| title_full_unstemmed | Soft biometrics: gender recognition from unconstrained face images using local feature descriptor |
| title_short | Soft biometrics: gender recognition from unconstrained face images using local feature descriptor |
| title_sort | soft biometrics: gender recognition from unconstrained face images using local feature descriptor |
| url | http://psasir.upm.edu.my/id/eprint/34788/ http://psasir.upm.edu.my/id/eprint/34788/ http://psasir.upm.edu.my/id/eprint/34788/1/Soft%20biometrics%20gender%20recognition%20from%20unconstrained%20face%20images%20using%20local%20feature%20descriptor.pdf |