Learning discriminative features from RGB-D images for gender and ethnicity identification

Safaa Azzakhnini; Lahoucine Ballihi; Driss Aboutajdine

doi:10.1117/1.JEI.25.6.061625

22 December 2016 Learning discriminative features from RGB-D images for gender and ethnicity identification

Safaa Azzakhnini, Lahoucine Ballihi, Driss Aboutajdine

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 6, 061625 (December 2016). https://doi.org/10.1117/1.JEI.25.6.061625

Abstract

The development of sophisticated sensor technologies gave rise to an interesting variety of data. With the appearance of affordable devices, such as the Microsoft Kinect, depth-maps and three-dimensional data became easily accessible. This attracted many computer vision researchers seeking to exploit this information in classification and recognition tasks. In this work, the problem of face classification in the context of RGB images and depth information (RGB-D images) is addressed. The purpose of this paper is to study and compare some popular techniques for gender recognition and ethnicity classification to understand how much depth data can improve the quality of recognition. Furthermore, we investigate which combination of face descriptors, feature selection methods, and learning techniques is best suited to better exploit RGB-D images. The experimental results show that depth data improve the recognition accuracy for gender and ethnicity classification applications in many use cases.

Citation Download Citation

Safaa Azzakhnini, Lahoucine Ballihi, and Driss Aboutajdine "Learning discriminative features from RGB-D images for gender and ethnicity identification," Journal of Electronic Imaging 25(6), 061625 (22 December 2016). https://doi.org/10.1117/1.JEI.25.6.061625

Received: 30 April 2016; Accepted: 21 November 2016; Published: 22 December 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available