| Issue |
MATEC Web Conf.
Volume 125, 2017
21st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|
|
|---|---|---|
| Article Number | 02067 | |
| Number of page(s) | 5 | |
| Section | Systems | |
| DOI | https://doi.org/10.1051/matecconf/201712502067 | |
| Published online | 04 October 2017 | |
A performance comparison of two emotion-recognition implementations using OpenCV and Cognitive Services API
Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlín, Nad Stráněmi 4511, 76005 Zlín, Czech Republic
a Corresponding author: beltran_prieto@fai.utb.cz
Emotions represent feelings about people in several situations. Various machine learning algorithms have been developed for emotion detection in a multimedia element, such as an image or a video. These techniques can be measured by comparing their accuracy with a given dataset in order to determine which algorithm can be selected among others. This paper deals with the comparison of two implementations of emotion recognition in faces, each implemented with specific technology. OpenCV is an open-source library of functions and packages mostly used for computer-vision analysis and applications. Cognitive services is a set of APIs containing artificial intelligence algorithms for computer-vision, speech, knowledge, and language processing. Two Android mobile applications were developed in order to test the performance between an OpenCV algorithm for emotion recognition and an implementation of Emotion cognitive service. For this research, one thousand tests were carried out per experiment. Our findings show that the OpenCV implementation got a better performance than the Cognitive services application. In both cases, performance can be improved by increasing the sample size per emotion during the training step.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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