Facial Emotion Recognition for Mobile Devices: A Practical Review

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Facial Emotion Recognition for Mobile Devices: A Practical Review

Popis výsledku v původním jazyce

Communicating via email or various chat applications on smartphones is part of most people&apos;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.

Název v anglickém jazyce

Facial Emotion Recognition for Mobile Devices: A Practical Review

Popis výsledku anglicky

Communicating via email or various chat applications on smartphones is part of most people&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

12

Číslo periodika v rámci svazku

25 January 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

15735-15747

Kód UT WoS článku

001161068800001

EID výsledku v databázi Scopus

2-s2.0-85183951109