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Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition
Ugail, Hassan Al-dahoud, Ahmad
Ugail, Hassan
Al-dahoud, Ahmad
Publication Date
2018-09
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© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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2018-02-20
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Abstract
Automatic gender classification has become a topic of great interest to the visual computing research community in recent
times. This is due to the fact that computer-based automatic gender recognition has multiple applications including, but not
limited to, face perception, age, ethnicity, identity analysis, video surveillance and smart human computer interaction. In this
paper, we discuss a machine learning approach for efficient identification of gender purely from the dynamics of a person’s
smile. Thus, we show that the complex dynamics of a smile on someone’s face bear much relation to the person’s gender.
To do this, we first formulate a computational framework that captures the dynamic characteristics of a smile. Our dynamic
framework measures changes in the face during a smile using a set of spatial features on the overall face, the area of the
mouth, the geometric flow around prominent parts of the face and a set of intrinsic features based on the dynamic geometry
of the face. This enables us to extract 210 distinct dynamic smile parameters which form as the contributing features for
machine learning. For machine classification, we have utilised both the Support Vector Machine and the k-Nearest Neighbour
algorithms. To verify the accuracy of our approach, we have tested our algorithms on two databases, namely the CK+ and the
MUG, consisting of a total of 109 subjects. As a result, using the k-NN algorithm, along with tenfold cross validation, for
example, we achieve an accurate gender classification rate of over 85%. Hence, through the methodology we present here,
we establish proof of the existence of strong indicators of gender dimorphism, purely in the dynamics of a person’s smile.
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Citation
Ugail H and Al-dahoud A (2018) Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition. The Visual Computer. 34(9): 1243-1254.
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