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SWOT Analysis of Behavioural Recognition Through Variable Modalities

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Ubiquitous Intelligent Systems (ICUIS 2021)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 302))

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Abstract

In this era of technology, recognising the human emotion to improve the human–machine interaction has become very important. The applications of emotion recognition have continued to grow in the past few years, from healthcare and security to improving customer care service and enhancing the user experience. With increased innovation, the number of use cases for this area would only grow more in the future. This forms the motivation for thoroughly analysing the market for recognition of human behaviour. The paper summarises the SWOT analysis of behavioural recognition through the use of variable modalities. This paper gives an overview of the currently employed techniques which involve the extraction of features from text, audio and video. The main objective is to get an overview of the internal potential and limitations of this branch of machine learning. The analysis brings out the merits and demerits of all the techniques discussed.

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Authors and Affiliations

  1. Department of Software Engineering, Delhi Technological University, Delhi, India

    Abhilasha Sharma, Aakash Garg, Akshat Thapliyal & Abhishek Rajput

Authors
  1. Abhilasha Sharma
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  2. Aakash Garg
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  3. Akshat Thapliyal
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  4. Abhishek Rajput
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Corresponding author

Correspondence to Aakash Garg .

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Editors and Affiliations

  1. Department of EEE, Shree Venkateshwara Hi-Tech Engg College, Gobichettipalayam, India

    P. Karuppusamy

  2. Castilla-La Mancha University, Ciudad Real, Spain

    Fausto Pedro García Márquez

  3. Department of Computer Science, Kennesaw State University, Marietta, GA, USA

    Tu N. Nguyen

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Sharma, A., Garg, A., Thapliyal, A., Rajput, A. (2022). SWOT Analysis of Behavioural Recognition Through Variable Modalities. In: Karuppusamy, P., García Márquez, F.P., Nguyen, T.N. (eds) Ubiquitous Intelligent Systems. ICUIS 2021. Smart Innovation, Systems and Technologies, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-2541-2_25

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