Soft biometrics: Gender recognition from unconstrained face images using local feature descriptor

Olasimbo Ayodeji Arigbabu, Sharifah Mumtazah Syed Ahmad, Wan Azizun Wan Adnan, Salman Yussof, Saif Mahmood

Research output: Contribution to journalArticle

3 Citations (Scopus)


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 classifi er, 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.

Original languageEnglish
Pages (from-to)111-122
Number of pages12
JournalJournal of Information and Communication Technology
Issue number1
Publication statusPublished - 01 Jan 2015


All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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