Performance Analysis of Emotion Recognition Prediction on Mobile Devices

Jonathan Fabricio Pillajo Pilaguano; Pamela Elizabeth Tello Arévalo; Flavio Vinicio Changoluisa Panchi

doi:10.1007/978-3-031-32213-6_6

Performance Analysis of Emotion Recognition Prediction on Mobile Devices

Jonathan Fabricio Pillajo Pilaguano, Pamela Elizabeth Tello Arévalo, Flavio Vinicio Changoluisa Panchi

Electronics and Telematics Research Group (GIETEC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Artificial Intelligence (AI) has had a boom in recent years thanks to technological development, it is increasingly used in various fields of research and many of these use mobile devices. Emotion recognition is one of them, as it helps in neuroscience, computer science, and medical applications. There is information where applications that are developed on mobile devices for emotion recognition, but none considers the performance of the algorithm used. The purpose of this article was to evaluate how it affects the performance of a mobile application based on Artificial Intelligence (AI) for emotion recognition according to the properties of mobile devices of different ranges. For this purpose, two different Convolutional Neural Network (CNN) architectures are evaluated, which will be analyzed according to the following metrics: response time, RAM, CPU usage, and accuracy. The results show that a deep layered CNN has better performance and lower computational cost compared to a conventional CNN on mobile devices.

Original language	English
Title of host publication	Smart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers
Editors	Fabián R. Narváez, Fernando Urgilés, Juan Pablo Salgado-Guerrero, Teodiano Freire Bastos-Filho
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	77-90
Number of pages	14
ISBN (Print)	9783031322129
DOIs	https://doi.org/10.1007/978-3-031-32213-6_6
State	Published - 2023
Event	3rd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022 - Cuenca, Ecuador Duration: 16 Nov 2022 → 18 Nov 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1705 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022
Country/Territory	Ecuador
City	Cuenca
Period	16/11/22 → 18/11/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Convolutional Neural Networks
Emotion recognition
Fer-2013
MobileNet

Access to Document

10.1007/978-3-031-32213-6_6

Cite this

Pillajo Pilaguano, J. F., Tello Arévalo, P. E., & Panchi, F. V. C. (2023). Performance Analysis of Emotion Recognition Prediction on Mobile Devices. In F. R. Narváez, F. Urgilés, J. P. Salgado-Guerrero, & T. F. Bastos-Filho (Eds.), Smart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers (pp. 77-90). (Communications in Computer and Information Science; Vol. 1705 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-32213-6_6

Pillajo Pilaguano, Jonathan Fabricio ; Tello Arévalo, Pamela Elizabeth ; Panchi, Flavio Vinicio Changoluisa. / Performance Analysis of Emotion Recognition Prediction on Mobile Devices. Smart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers. editor / Fabián R. Narváez ; Fernando Urgilés ; Juan Pablo Salgado-Guerrero ; Teodiano Freire Bastos-Filho. Springer Science and Business Media Deutschland GmbH, 2023. pp. 77-90 (Communications in Computer and Information Science).

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abstract = "Artificial Intelligence (AI) has had a boom in recent years thanks to technological development, it is increasingly used in various fields of research and many of these use mobile devices. Emotion recognition is one of them, as it helps in neuroscience, computer science, and medical applications. There is information where applications that are developed on mobile devices for emotion recognition, but none considers the performance of the algorithm used. The purpose of this article was to evaluate how it affects the performance of a mobile application based on Artificial Intelligence (AI) for emotion recognition according to the properties of mobile devices of different ranges. For this purpose, two different Convolutional Neural Network (CNN) architectures are evaluated, which will be analyzed according to the following metrics: response time, RAM, CPU usage, and accuracy. The results show that a deep layered CNN has better performance and lower computational cost compared to a conventional CNN on mobile devices.",

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Pillajo Pilaguano, JF, Tello Arévalo, PE & Panchi, FVC 2023, Performance Analysis of Emotion Recognition Prediction on Mobile Devices. in FR Narváez, F Urgilés, JP Salgado-Guerrero & TF Bastos-Filho (eds), Smart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1705 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 77-90, 3rd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022, Cuenca, Ecuador, 16/11/22. https://doi.org/10.1007/978-3-031-32213-6_6

Performance Analysis of Emotion Recognition Prediction on Mobile Devices. / Pillajo Pilaguano, Jonathan Fabricio; Tello Arévalo, Pamela Elizabeth; Panchi, Flavio Vinicio Changoluisa.
Smart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers. ed. / Fabián R. Narváez; Fernando Urgilés; Juan Pablo Salgado-Guerrero; Teodiano Freire Bastos-Filho. Springer Science and Business Media Deutschland GmbH, 2023. p. 77-90 (Communications in Computer and Information Science; Vol. 1705 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tello Arévalo, Pamela Elizabeth

AU - Panchi, Flavio Vinicio Changoluisa

PY - 2023

Y1 - 2023

N2 - Artificial Intelligence (AI) has had a boom in recent years thanks to technological development, it is increasingly used in various fields of research and many of these use mobile devices. Emotion recognition is one of them, as it helps in neuroscience, computer science, and medical applications. There is information where applications that are developed on mobile devices for emotion recognition, but none considers the performance of the algorithm used. The purpose of this article was to evaluate how it affects the performance of a mobile application based on Artificial Intelligence (AI) for emotion recognition according to the properties of mobile devices of different ranges. For this purpose, two different Convolutional Neural Network (CNN) architectures are evaluated, which will be analyzed according to the following metrics: response time, RAM, CPU usage, and accuracy. The results show that a deep layered CNN has better performance and lower computational cost compared to a conventional CNN on mobile devices.

AB - Artificial Intelligence (AI) has had a boom in recent years thanks to technological development, it is increasingly used in various fields of research and many of these use mobile devices. Emotion recognition is one of them, as it helps in neuroscience, computer science, and medical applications. There is information where applications that are developed on mobile devices for emotion recognition, but none considers the performance of the algorithm used. The purpose of this article was to evaluate how it affects the performance of a mobile application based on Artificial Intelligence (AI) for emotion recognition according to the properties of mobile devices of different ranges. For this purpose, two different Convolutional Neural Network (CNN) architectures are evaluated, which will be analyzed according to the following metrics: response time, RAM, CPU usage, and accuracy. The results show that a deep layered CNN has better performance and lower computational cost compared to a conventional CNN on mobile devices.

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Pillajo Pilaguano JF, Tello Arévalo PE, Panchi FVC. Performance Analysis of Emotion Recognition Prediction on Mobile Devices. In Narváez FR, Urgilés F, Salgado-Guerrero JP, Bastos-Filho TF, editors, Smart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 77-90. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-32213-6_6

Performance Analysis of Emotion Recognition Prediction on Mobile Devices

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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