Cognitive Model - Based Emotion Recognition From Facial Expressions For Live Human Computer Interaction

Bindu, M. Hima; Gupta, Parnika; Tiwary, Uma Shanker

doi:10.1109/ciisp.2007.369194

Cited by 25 publications

(6 citation statements)

References 6 publications

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“…Their experiments confirmed the existence of a good correlation between basic movements of the facial action units [13], [19] and facial expressions [1], [2], [5], [7], [10], [19]- [22]. Kobayashi and Hara [15]- [17] designed a scheme for the recognition of human facial expressions using the well-known back-propagation neural networks [38], [43].…”

Section: Introductionmentioning

confidence: 90%

General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

Halder

Konar

Mandal

et al. 2013

IEEE Trans. Syst. Man Cybern, Syst.

103

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Facial expressions of a person representing similar emotion are not always unique. Naturally, the facial features of a subject taken from different instances of the same emotion have wide variations. In the presence of two or more facial features, the variation of the attributes together makes the emotion recognition problem more complicated. This variation is the main source of uncertainty in the emotion recognition problem, which has been addressed here in two steps using type-2 fuzzy sets. First a type-2 fuzzy face space is constructed with the background knowledge of facial features of different subjects for different emotions. Second, the emotion of an unknown facial expression is determined based on the consensus of the measured facial features with the fuzzy face space. Both interval and general type-2 fuzzy sets (GT2FS) have been used separately to model the fuzzy face space. The interval type-2 fuzzy set (IT2FS) involves primary membership functions for m facial features obtained from n-subjects, each having l-instances of facial expressions for a given emotion. The GT2FS in addition to employing the primary membership functions mentioned above also involves the secondary memberships for individual primary membership curve, which has been obtained here by formulating and solving an optimization problem. The optimization problem here attempts to minimize the difference between two decoded signals: the first one being the type-1 defuzzification of the average primary membership functions obtained from the n-subjects, while the second one refers to the type-2 defuzzified signal for a given primary membership function with secondary memberships as unknown. The uncertainty management policy adopted using GT2FS has resulted in a classification accuracy of 98.333% in comparison to 91.667% obtained by its interval type-2 counterpart. A small improvement (approximately 2.5%) in classification accuracy by IT2FS has been attained by pre-processing measurements using - he well-known interval approach

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Section: Introductionmentioning

confidence: 90%

General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

Halder

Konar

Mandal

et al. 2013

IEEE Trans. Syst. Man Cybern, Syst.

103

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show abstract

“…[80] and [100] epitomize crucial feelings with the sub-builds of positive and negative emotions, i.e., outrage, dread, happiness, pity, love, sicken, shock, tension, sympathy, etc. [9] further enriched the dialog with an array of some other essential feelings, i.e., pride, bliss, desire, distress, rage, vengeance, anguish, disgrace, energy, and despise coupled with the essential feelings suggested by [80] and [100]. [9] further assure that such essential feelings can also bring forth complex feelings, which can include regret, pardoning, sentimentality, desire, cheer, offense, frustration, trust, etc.…”

Section: Decision Making For Celebrity Branding (Endorsement)mentioning

confidence: 99%

“…[9] further enriched the dialog with an array of some other essential feelings, i.e., pride, bliss, desire, distress, rage, vengeance, anguish, disgrace, energy, and despise coupled with the essential feelings suggested by [80] and [100]. [9] further assure that such essential feelings can also bring forth complex feelings, which can include regret, pardoning, sentimentality, desire, cheer, offense, frustration, trust, etc. Be that as it may, [29] contentions are worth being referenced here, by which they mention the comprehensiveness of the essential feelings, i.e., joy, pride, pity, dread, outrage, disturb, and shock.…”

Section: Decision Making For Celebrity Branding (Endorsement)mentioning

confidence: 99%

Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)

Nikseresht¹,

Raeisi²,

Mohammadi³

2021

Preprint

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The volume of discussions concerning brands within social media provides digital marketers with great opportunities for tracking and analyzing the feelings and views of consumers toward brands, products, influencers, services, and ad campaigns in the consumer-generated content (CGC). The aim of the present study is to assess and compare the performance of firms and celebrities (i.e., influencers that with the experience of being in an ad campaign of those companies) with the automated sentiment analysis that was employed for CGC at social media while exploring the feeling of the consumers toward them to observe which influencer (of two for each company) had a closer effect with the corresponding corporation on consumer minds. For this purpose, a number of consumer tweets from the pages of brands and influencers were utilized to make a comparison of machine learning and lexicon-based approaches to the sentiment analysis through the Naïve algorithm (lexicon-based) and Naïve Bayes algorithm (machine learning method) and obtain the desired results to assess the campaigns. The findings suggested that the approaches were dissimilar in terms of accuracy; the machine learning method (Naïve Bayes algorithm) yielded higher accuracy. Finally, the results showed which influencer was more appropriate according to their existence in previous campaigns and helped choose the right influencer in the future for our company and have a better, more appropriate, and more efficient ad campaign subsequently. It is required to conduct further studies on the accuracy improvement of the sentiment classification. This approach should be employed for other social media CGC types, e.g., Instagram feeds. The results revealed decisionmaking for which sentiment analysis methods (or their combinations) are the best approaches for the analysis of social media. It was also found that companies should be aware of their consumers' sentiments and choose the right person every time they think of a campaign.

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“…Envick [23] proposed a cognitive model for developing entrepreneurial intelligence. Bindu et al [24] proposed a 3D cognitive model to classify emotions' uncertainty, contradiction, and cognitive nature. The model can classify 22 emotions subject to a facial expression database.…”

Section: Related Workmentioning

confidence: 99%

Cognitive and academic-based probability models for predicting campus placements

Alfana

Jammalamadaka

Prakash

et al. 2022

IJ-AI

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<span>Industrial organizations select the students for placement by conducting tests based on the academic content and targeting students' cognitive levels, such as the problem-solving ability. Educational institutes are mostly dependent on the students' academic performance to judge the likelihood of Employing the students. Cognitive and academic-based models are required to accurately predict the students' employment and assess the areas of improvement required. The interrelationships must be established to achieve coherence between the models. In this paper, three predictive models have been presented, which are based on: cognitive factors, Academic factors with and without anomaly correction. The models will help the educational institutions prepare the students for the highest number of placements. The models provide the basis for prediction on the individual subject/factor basis and the overall prediction considering all the subjects/cognitive factors. 98% accuracy in predicting the placement of the students has been achieved considering both the cognitive and Academic models with a built-in anomaly correction mechanism. The anomaly correction mechanism presented in the paper improved the accuracy of prediction from 92% to 98%. The positive correlation between the cognitive and Academic model helps inferencing one model from the other.</span>

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Cognitive Model - Based Emotion Recognition From Facial Expressions For Live Human Computer Interaction

Cited by 25 publications

References 6 publications

General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)

Cognitive and academic-based probability models for predicting campus placements

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