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Symmetry 2018, 10(9), 387;

Smart Doll: Emotion Recognition Using Embedded Deep Learning

VISILAB, University of Castilla-La Mancha, E.T.S.I. Industriales, Avda Camilo Jose Cela s/n, 13071 Ciudad Real, Spain
nViso SA, PSE-D, Site EPFL, CH-1015 Lausanne, Switzerland
Intel R&D Ireland Ltd., Collinstown Industrial Park, Leixlip, Co Kildare W23 CW68, Ireland
Author to whom correspondence should be addressed.
Received: 13 July 2018 / Revised: 2 August 2018 / Accepted: 7 September 2018 / Published: 7 September 2018
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Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll. View Full-Text
Keywords: facial informatics; deep learning; computer vision; mobile applications; real-time and embedded systems facial informatics; deep learning; computer vision; mobile applications; real-time and embedded systems

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Espinosa-Aranda, J.L.; Vallez, N.; Rico-Saavedra, J.M.; Parra-Patino, J.; Bueno, G.; Sorci, M.; Moloney, D.; Pena, D.; Deniz, O. Smart Doll: Emotion Recognition Using Embedded Deep Learning. Symmetry 2018, 10, 387.

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