22 December 2016 Learning discriminative features from RGB-D images for gender and ethnicity identification
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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.
© 2016 SPIE and IS&T
Safaa Azzakhnini, Safaa Azzakhnini, Lahoucine Ballihi, Lahoucine Ballihi, Driss Aboutajdine, 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 . Submission: Received: 30 April 2016; Accepted: 21 November 2016
Received: 30 April 2016; Accepted: 21 November 2016; Published: 22 December 2016
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