A performance comparison of two emotion-recognition implementations using OpenCV and Cognitive Services API
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63517243" target="_blank" >RIV/70883521:28140/17:63517243 - isvavai.cz</a>
Result on the web
<a href="https://www.matec-conferences.org/articles/matecconf/pdf/2017/39/matecconf_cscc2017_02067.pdf" target="_blank" >https://www.matec-conferences.org/articles/matecconf/pdf/2017/39/matecconf_cscc2017_02067.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1051/matecconf/20171250" target="_blank" >10.1051/matecconf/20171250</a>
Alternative languages
Result language
angličtina
Original language name
A performance comparison of two emotion-recognition implementations using OpenCV and Cognitive Services API
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 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)
Result continuities
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)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
MATEC Web of Conferences
ISBN
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ISSN
2261-236X
e-ISSN
neuvedeno
Number of pages
5
Pages from-to
"nestrankovano"
Publisher name
EDP Sciences
Place of publication
Les Ulis
Event location
Heraklion, Crete
Event date
Jul 14, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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