Comparing the Performance of Emotion-Recognition Implementations in OpenCV, Cognitive Services, and Google Vision APIs

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63517247" target="_blank" >RIV/70883521:28140/17:63517247 - isvavai.cz</a>

Result on the web

<a href="http://www.wseas.org/multimedia/journals/information/2017/a405909-078.pdf" target="_blank" >http://www.wseas.org/multimedia/journals/information/2017/a405909-078.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Comparing the Performance of Emotion-Recognition Implementations in OpenCV, Cognitive Services, and Google Vision APIs

Original language description

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 three 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, as well as Google Cloud AI, are sets of APIs which provide machine learning and artificial intelligence algorithms to develop smart applications capable of integrate computer-vision, speech, knowledge, and language processing features. Three Android mobile applications were developed in order to test the performance between an OpenCV algorithm for emotion recognition, an implementation of Emotion cognitive service, and a Google Cloud Vision deployment for emotion-detection in faces. For this research, one thousand tests were carried out per experiment. Our findings show that the OpenCV implementation got the best performance, which can be improved by increasing the sample size per emotion during the training step.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

WSEAS Transactions on Information Science and Applications

ISSN

1790-0832

e-ISSN

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Volume of the periodical

2017

Issue of the periodical within the volume

14

Country of publishing house

GR - GREECE

Number of pages

7

Pages from-to

184-190

UT code for WoS article

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EID of the result in the Scopus database

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