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.

Bianco, S., Celona, L., Schettini, R. (2016). Robust smile detection using convolutional neural networks. JOURNAL OF ELECTRONIC IMAGING, 25(6) [10.1117/1.JEI.25.6.063002].

Robust smile detection using convolutional neural networks

BIANCO, SIMONE
Primo
;
CELONA, LUIGI
Secondo
;
SCHETTINI, RAIMONDO
Ultimo
2016

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.
Articolo in rivista - Articolo scientifico
convolutional neural networks; deep learning; face alignment; face detection; smile detection;
smile detection; deep learning; convolutional neural networks; face detection; face alignment
English
2016
25
6
063002
none
Bianco, S., Celona, L., Schettini, R. (2016). Robust smile detection using convolutional neural networks. JOURNAL OF ELECTRONIC IMAGING, 25(6) [10.1117/1.JEI.25.6.063002].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/135697
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