Soft Biometrics: Gender Recognition from Unconstrained Face Images Using Local Feature Descriptor

Authors

  • Olasimbo Ayodeji Arigbabu Department of Computer and Communication Systems, Universiti Putra Malaysia, Malaysia
  • Sharifah Mumtazah Syed Ahmad Department of Computer and Communication Systems, Universiti Putra Malaysia, Malaysia
  • Wan Azizun Wan Adnan Department of Computer and Communication Systems, Universiti Putra Malaysia, Malaysia
  • Saif Mahmood Department of Computer and Communication Systems, Universiti Putra Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jict2015.14.7

Keywords:

Gender recognition, unconstrained face images, soft biometric traits, local feature descriptor, shape feature extraction

Abstract

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.

 

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Published

28-04-2015

Issue

Vol. 14 (2015): Journal of Information and Communication Technology (JICT) Vol. 14, 2015

Section

Articles

How to Cite

Arigbabu, O. A., Syed Ahmad, S. M., Wan Adnan, W. A., & Mahmood, S. (2015). Soft Biometrics: Gender Recognition from Unconstrained Face Images Using Local Feature Descriptor. Journal of Information and Communication Technology, 14, 111-122. https://doi.org/10.32890/jict2015.14.7

Research impact

Harvested 2026-09-06
7 citations, from OpenAlex — the highest of the sources checked

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Identifiers DOI 10.32890/jict2015.14.7 OpenAlex W2950016656