Arigbabu, Olasimbo Ayodeji and Syed Ahmad, Sharifah Mumtazah and Wan Adnan, Wan Azizun and Yussof, Salman and Mahmood, Saif (2015) Soft biometrics: Gemder recognition from unconstrained face images using local feature descriptor. Journal of Information and Communication Technology (JICT), 14. pp. 111-122. ISSN 1675-414X

PDF Restricted to Registered users only Download (248kB) | Request a copy

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.

Item Type:	Article
Uncontrolled Keywords:	Gender recognition, unconstrained face images, soft biometric traits, local feature descriptor, shape feature extraction.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	29 Apr 2018 01:44
Last Modified:	29 Apr 2018 01:44
URI:	https://repo.uum.edu.my/id/eprint/24083

Actions (login required)

View Item