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Robust smile detection using convolutional neural networks

[+] Author Affiliations
Simone Bianco, Luigi Celona, Raimondo Schettini

University of Milano-Bicocca, Dipartimento di Informatica, Sistemistica e Comunicazione, Viale Sarca 336, 20126 Milano, Italy

J. Electron. Imaging. 25(6), 063002 (Nov 14, 2016). doi:10.1117/1.JEI.25.6.063002
History: Received August 24, 2016; Accepted October 24, 2016
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Abstract.  We present a fully automated approach for smile detection. Faces are detected using a multiview face detector and aligned and scaled using automatically detected eye locations. Then, we use a convolutional neural network (CNN) to determine whether it is a smiling face or not. To this end, we investigate different shallow CNN architectures that can be trained even when the amount of learning data is limited. We evaluate our complete processing pipeline on the largest publicly available image database for smile detection in an uncontrolled scenario. We investigate the robustness of the method to different kinds of geometric transformations (rotation, translation, and scaling) due to imprecise face localization, and to several kinds of distortions (compression, noise, and blur). To the best of our knowledge, this is the first time that this type of investigation has been performed for smile detection. Experimental results show that our proposal outperforms state-of-the-art methods on both high- and low-quality images.

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Citation

Simone Bianco ; Luigi Celona and Raimondo Schettini
"Robust smile detection using convolutional neural networks", J. Electron. Imaging. 25(6), 063002 (Nov 14, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.063002


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