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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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