Gender recognition from facial images: 2D or 3D?

Zhang, W., Smith, M., Smith, L. and Farooq, A. (2016) Gender recognition from facial images: 2D or 3D? Journal of the Optical Society of America A, 33 (3). pp. 333-344. ISSN 1084-7529 Available from:

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This paper seeks to compare encoded features from both 2D and 3D face images in order to achieve automatic gender recognition with high accuracy and robustness. The Fisher Vector encoding method is employed to produce 2D, 3D and fused features with escalated discriminative power. For 3D face analysis, a two-source Photometric Stereo (PS) method is introduced that enables 3D surface reconstructions with accurate details as well as desirable efficiency. Moreover, a 2D+3D imaging device, taking the two-source PS method as its core, has been developed that can simultaneously gather colour images for 2D evaluations and PS images for 3D analysis. This system inherits the superior reconstruction accuracy from the standard (3 or more light) PS method, but simplifies the reconstruction algorithm as well as the hardware design by only requiring two light sources. It also offers great potential for facilitating human computer interaction by being accurate, cheap, efficient and non-intrusive. 10 types of low-level 2D and 3D features have been experimented with and encoded for Fisher Vector gender recognition. Evaluations of the Fisher Vector encoding method have been performed on the FERET database, Colour FERET database, LFW database and FRGCv2 database, yielding 97.7%, 98.0%, 92.5% and 96.7% accuracy, respectively. In addition, the comparison of 2D and 3D features has been drawn from a self-collected dataset, which is constructed with the aid of the 2D+3D imaging device in a series of data capture experiments. With a variety of experiments and evaluations, it can be proved that the Fisher Vector encoding method outperforms most state-of-the-art gender recognition methods. It has also been observed that 3D features reconstructed by the two-source PS method are able to further boost the Fisher Vector gender recognition performance, i.e. up to a 6% increase on the self-collected database.

Item Type:Article
Uncontrolled Keywords:gender recognition, computers, 2D, 3D
Faculty/Department:Faculty of Environment and Technology > Department of Engineering Design and Mathematics
ID Code:28147
Deposited By: W. Zhang
Deposited On:08 Feb 2016 08:53
Last Modified:07 Apr 2016 14:52

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