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Gender recognition from facial images: Two or three dimensions?

Zhang, Wenhao; Smith, Melvyn L.; Smith, Lyndon N.; Farooq, Abdul


Wenhao Zhang
Senior Lecturer in Machine Vision

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Melvyn Smith
Research Centre Director Vision Lab/Prof

Lyndon Smith
Professor in Computer Simulation and Machine

Abdul Farooq
Associate Head of Departmemt Business Engagement and Partnerships


© 2016 Optical Society of America. This paper seeks to compare encoded features from both two-dimensional (2D) and three-dimensional (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 color images for 2D evaluations and PS images for 3D analysis. This system inherits the superior reconstruction accuracy from the standard (three 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 nonintrusive. Ten 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, Color 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.


Smith, L. N., Smith, M. L., Wenhao, Z., Zhang, W., Smith, M., Smith, L., & Farooq, A. (2016). Gender recognition from facial images: Two or three dimensions?. Journal of the Optical Society of America A, 33(3), 333-344.

Journal Article Type Article
Acceptance Date Dec 18, 2015
Online Publication Date Mar 1, 2016
Publication Date Mar 1, 2016
Journal Journal of the Optical Society of America A: Optics and Image Science, and Vision
Print ISSN 1084-7529
Electronic ISSN 1520-8532
Publisher Optical Society of America
Peer Reviewed Peer Reviewed
Volume 33
Issue 3
Pages 333-344
Keywords gender recognition, computers, 2D, 3D
Public URL
Publisher URL


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