Emotion Recognition and Detection Methods: A Comprehensive Survey

Saxena, Anvita; Khanna, Ashish; Gupta, Deepak

doi:10.33969/ais.2020.21005

Cited by 109 publications

(58 citation statements)

References 0 publications

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“…We got the impression that the facial emotion recognition did not work as we expected it at all, which is surprising since, in general, facial emotion recognition is supposed to perform better than speech emotion recognition [19] and achieves accuracies up to 98 % percent in other studies (cf. Saxena et al [38]). Some counterintuitive results (e. g. the correlation direction of happiness) lead us to the assumption that certain facial expressions for certain situations in usability tests might not resemble what they usually mean.…”

Section: Tool Performancementioning

confidence: 98%

“…Facial emotion recognition offers a plethora of machine learning techniques and standardized training corpora (cf. Saxena et al [38]). However, when analyzing the training images it becomes clear that they are far apart from the setting and the facial expressions of a usability test.…”

Section: Emotion Recognition and Usability Engineeringmentioning

confidence: 98%

“…Speech analysis, on the other hand can achieve similar accuracies but is on average regarded as inferior (cf. Hudlick [19]; Saxena et al [38]). The popularity of these methods has led to multiple commercial and non-commercial tools like Affectiva [25] or OpenFace [3].…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, we see the most value in the exploration of more advanced emotion recognition approaches adjusted to our task. For this study, we used general-purpose tools; the trend in the research area is however to adjust and fine-tune methods to a specific use case if the use case is very unusual [38]. This is obviously the case for usability tests and the results show that the tools are trained and designed for other use cases.…”

Section: Emotion Recognition and Usability Engineeringmentioning

confidence: 99%

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Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Schmidt

Schlindwein

Lichtner

et al. 2020

I-Com

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Due to progress in affective computing, various forms of general purpose sentiment/emotion recognition software have become available. However, the application of such tools in usability engineering (UE) for measuring the emotional state of participants is rarely employed. We investigate if the application of sentiment/emotion recognition software is beneficial for gathering objective and intuitive data that can predict usability similar to traditional usability metrics. We present the results of a UE project examining this question for the three modalities text, speech and face. We perform a large scale usability test (N = 125) with a counterbalanced within-subject design with two websites of varying usability. We have identified a weak but significant correlation between text-based sentiment analysis on the text acquired via thinking aloud and SUS scores as well as a weak positive correlation between the proportion of neutrality in users’ voice and SUS scores. However, for the majority of the output of emotion recognition software, we could not find any significant results. Emotion metrics could not be used to successfully differentiate between two websites of varying usability. Regression models, either unimodal or multimodal could not predict usability metrics. We discuss reasons for these results and how to continue research with more sophisticated methods.

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Section: Tool Performancementioning

confidence: 98%

Section: Emotion Recognition and Usability Engineeringmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Emotion Recognition and Usability Engineeringmentioning

confidence: 99%

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Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Schmidt

Schlindwein

Lichtner

et al. 2020

I-Com

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“…The possible causes for global warming are anthropogenic activities like escalation in the combination of aerosols and green gases, along with alteration in land use cover as per IPCC report. The previous done observations suggested that temperature increases with the increase in elevation which makes plateau and mountain range susceptible to global warming [35]. India shows an increasing trend of mean annual temperature from 1903-2003 of around 0.50C/100yrs.…”

Section: Climate Change Observationmentioning

confidence: 98%

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Malik¹,

Pande²,

Nishi³

et al. 2020

AIS

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Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns. Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation. Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn't happened. For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics. Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events. While ESM development is actually completely necessary, a parallel concentrate on utilizing ML and AI to determine as well as capitalize a great deal more on pre pre-existing simulations as well as info is suggested by us.

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