Facial Emotion Recognition for Mobile Devices: A Practical Review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254297" target="_blank" >RIV/61989100:27240/24:10254297 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10414102" target="_blank" >https://ieeexplore.ieee.org/document/10414102</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3358455" target="_blank" >10.1109/ACCESS.2024.3358455</a>
Alternative languages
Result language
angličtina
Original language name
Facial Emotion Recognition for Mobile Devices: A Practical Review
Original language description
Communicating via email or various chat applications on smartphones is part of most people's daily lives. But in written form, human communication loses a lot of valuable information, such as the facial expressions and emotions of the person you are communicating with. Thanks to techniques from the field of image processing, it is now possible to capture these non-verbal phenomena, and supplement written input with their non-verbal characteristics. In this paper, we explore the possibilities of emotion recognition from front camera images in mobile and embedded devices. A total of 63 classification and 28 regression models based on twelve different neural network architectures optimized for low performance mobile devices were trained and evaluated for success rate and latency. The training and evaluation of each neural network model is performed within the Keras API of the TensorFlow library and then converted to the TensorFlow Lite standard to reduce memory and computational requirements. Great care is taken to ensure that the entire process, from face detection to emotion classification, can operate in real time. To demonstrate and compare the performance of the evaluated models, a freely available optimized application running on Android mobile devices is created and published on Google Play, the source code of which is also available. (C) 2013 IEEE.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
12
Issue of the periodical within the volume
25 January 2024
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
15735-15747
UT code for WoS article
001161068800001
EID of the result in the Scopus database
2-s2.0-85183951109