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Can a smile reveal your gender?

Piotr Bilinski 1 Antitza Dantcheva 1 François Brémond 1 
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Automated gender estimation has numerous applications including video surveillance, human computer-interaction, anonymous customized advertisement and image retrieval. Most commonly, the underlying algorithms analyze facial appearance for clues of gender. In this work, we propose a novel approach for gender estimation, based on facial behavior in video-sequences capturing smiling subjects. The proposed behavioral approach quantifies gender dimorphism of facial smiling-behavior and is instrumental in cases of (a) omitted appearance-information (e.g. low resolution due to poor acquisition), (b) gender spoofing (e.g. makeup-based face alteration), as well as can be utilized to (c) improve the performance of appearance-based algorithms, since it provides complementary information. The proposed algorithm extracts spatio-temporal features based on dense trajectories, represented by a set of descriptors encoded by Fisher Vectors. Our results suggest that smile-based features include significant gender-clues. The designed algorithm obtains true gender classification rates of 86.3% for adolescents, significantly outperforming two state-of-the-art appearance-based algorithms (OpenBR and, while for adults we obtain true gender classification rates of 91.01%, which is comparably discriminative to the better of these appearance-based algorithms.
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Submitted on : Tuesday, October 25, 2016 - 11:11:06 AM
Last modification on : Saturday, June 25, 2022 - 11:23:55 PM


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  • HAL Id : hal-01387134, version 1



Piotr Bilinski, Antitza Dantcheva, François Brémond. Can a smile reveal your gender?. 15th International Conference of the Biometrics Special Interest Group (BIOSIG 2016), Sep 2016, Darmstadt, Germany. ⟨hal-01387134⟩



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