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A Comparative Study on Detection Accuracy of Cloud-Based Emotion Recognition Services

Authors:

Osamah M. Al-Omair,

Shihong HuangAuthors Info & Claims

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

Pages 142 - 148

https://doi.org/10.1145/3297067.3297079

Published: 28 November 2018 Publication History

Abstract

The ability of software systems adapting to human's input is a key element in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. This seamless integration will eliminate the barriers between human and machine. A critical requirement for co-adaptive systems is software system's ability to recognize human emotion, in which the system can detect and interpret users' emotions and adapt accordingly. There are numerous solutions that provide the technologies for emotion recognition. However, selecting an appropriate solution for a given task within a specific application domain can be challenging. The vast variation between these solutions makes the selecting task even more difficult. This paper compares cloud-based emotion recognition services offered by Amazon, Google, and Microsoft. These services detect human emotion through facial expression recognition with the utilization of computer vision. The focus of this paper is to measure the detection accuracy of these services. Accuracy is calculated based on the highest confidence rating returned by each service. All three services have been tested with the same dataset. This paper concludes with findings and recommendations based on our comparative analysis among these services.

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Cited By

Kumar DBhawna Narzary D(2024)Exploring the Utility of Emotion Recognition Systems in HealthcareUsing Machine Learning to Detect Emotions and Predict Human Psychology10.4018/979-8-3693-1910-9.ch011(245-271)Online publication date: 12-Apr-2024
https://doi.org/10.4018/979-8-3693-1910-9.ch011
Obwegeser DTimofte RMayer CBornstein MSchätzle MPatcas R(2024)Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressionsOrthodontics & Craniofacial Research10.1111/ocr.1282027:S2(25-32)Online publication date: 2-Jun-2024
https://doi.org/10.1111/ocr.12820
Ho Ro SLi YGong J(2024)A Machine learning approach for Post-Disaster data curationAdvanced Engineering Informatics10.1016/j.aei.2024.10242760(102427)Online publication date: Apr-2024
https://doi.org/10.1016/j.aei.2024.102427
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Index Terms

A Comparative Study on Detection Accuracy of Cloud-Based Emotion Recognition Services
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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cover image ACM Other conferences

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

November 2018

177 pages

ISBN:9781450366052

DOI:10.1145/3297067

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 November 2018

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SPML '18

SPML '18: 2018 International Conference on Signal Processing and Machine Learning

November 28 - 30, 2018

Shanghai, China

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Cited By

Kumar DBhawna Narzary D(2024)Exploring the Utility of Emotion Recognition Systems in HealthcareUsing Machine Learning to Detect Emotions and Predict Human Psychology10.4018/979-8-3693-1910-9.ch011(245-271)Online publication date: 12-Apr-2024
https://doi.org/10.4018/979-8-3693-1910-9.ch011
Obwegeser DTimofte RMayer CBornstein MSchätzle MPatcas R(2024)Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressionsOrthodontics & Craniofacial Research10.1111/ocr.1282027:S2(25-32)Online publication date: 2-Jun-2024
https://doi.org/10.1111/ocr.12820
Ho Ro SLi YGong J(2024)A Machine learning approach for Post-Disaster data curationAdvanced Engineering Informatics10.1016/j.aei.2024.10242760(102427)Online publication date: Apr-2024
https://doi.org/10.1016/j.aei.2024.102427
Al-Omair O(2023)Incorporating Eye-Tracking and Facial Expression Recognition for Preference Prediction in Recommender Systems2023 3rd International Conference on Computing and Information Technology (ICCIT)10.1109/ICCIT58132.2023.10273933(274-279)Online publication date: 13-Sep-2023
https://doi.org/10.1109/ICCIT58132.2023.10273933
Çini AJärvelä SDindar MMalmberg J(2023)How multiple levels of metacognitive awareness operate in collaborative problem solvingMetacognition and Learning10.1007/s11409-023-09358-718:3(891-922)Online publication date: 26-Sep-2023
https://doi.org/10.1007/s11409-023-09358-7
Al-Omair OHuang S(2022)An Emotional Support Robot Framework Using Emotion Recognition as Nonverbal Communication for Human-Robot Co-adaptationProceedings of the Future Technologies Conference (FTC) 2022, Volume 310.1007/978-3-031-18344-7_30(451-462)Online publication date: 14-Oct-2022
https://doi.org/10.1007/978-3-031-18344-7_30
Ahmad KWang SVogel CJain PO’Neill OSufi B(2021)Comparing the Performance of Facial Emotion Recognition Systems on Real-Life Videos: Gender, Ethnicity and AgeProceedings of the Future Technologies Conference (FTC) 2021, Volume 110.1007/978-3-030-89906-6_14(193-210)Online publication date: 24-Oct-2021
https://doi.org/10.1007/978-3-030-89906-6_14
Santamaria-Granados LMendoza-Moreno JRamirez-Gonzalez G(2020)Tourist Recommender Systems Based on Emotion Recognition—A Scientometric ReviewFuture Internet10.3390/fi1301000213:1(2)Online publication date: 24-Dec-2020
https://doi.org/10.3390/fi13010002
Al-Omair OHuang S(2019)A Comparative Study of Algorithms and Methods for Facial Expression Recognition2019 IEEE International Systems Conference (SysCon)10.1109/SYSCON.2019.8836770(1-6)Online publication date: Apr-2019
https://doi.org/10.1109/SYSCON.2019.8836770
Furey EBlue J(2019)The Emotographic Iceberg: Modelling Deep Emotional Affects Utilizing Intelligent Assistants and the IoT2019 19th International Conference on Computational Science and Its Applications (ICCSA)10.1109/ICCSA.2019.00037(175-180)Online publication date: Jul-2019
https://doi.org/10.1109/ICCSA.2019.00037
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