Emotion recognition using a Cauchy Naive Bayes classifier

Sebe, Nicu; Lew, Michael S.; Cohen, Ira L.; Garg, Ashutosh; Huang, Thomas S.

doi:10.1109/icpr.2002.1044578

Cited by 82 publications

(39 citation statements)

References 8 publications

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“…Some previous works have used SVM for recognizing drowsiness [28] or different emotions [9,22,24] from physiological features. Naïve Bayesian classifiers have been used for detecting human emotions from facial expressions [23] or physiological signals [4,9]. In this study, we have used these two types of classifiers for cognitive load classification of GSR and blink features.…”

Section: Introductionmentioning

confidence: 99%

GSR and Blink Features for Cognitive Load Classification

Nourbakhsh

Wang

Chen

2013

Lecture Notes in Computer Science

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Abstract.A system capable of monitoring its user's mental workload can evaluate the suitability of its interface and interactions for user's current cognitive status and properly change them when necessary. Galvanic skin response (GSR) and eye blinks are cognitive load measures which can be captured conveniently and at low cost. The present study has assessed multiple features of these two signals in classification of cognitive workload level. The experiment included arithmetic tasks with four difficulty levels and two types of machine learning algorithms have been applied for classification. Obtained results show that the studied features of blink and GSR can reasonably discriminate workload levels and combining features of the two modalities improves the accuracy of cognitive load classification. We have achieved around 75% for binary classification and more than 50% for four-class classification.

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Section: Introductionmentioning

confidence: 99%

GSR and Blink Features for Cognitive Load Classification

Nourbakhsh

Wang

Chen

2013

Lecture Notes in Computer Science

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“…SVM [13], NN [14], RF [15] and NB [16] are used in this work. The feature vectors formed are divided into training and test samples and they are fed to each of the classifiers separately for training and testing.…”

Section: Classificationmentioning

confidence: 99%

Geometrical Approach for Emotion Recognition from Facial Expressions Using 4D Videos and Analysis on Feature-Classifier Combination

Palaniswamy¹,

Tripathi²

2017

IJIES

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Emotion recognition from facial expressions using videos is important in human computer communication where the continuous changes in face movements need to be recognized efficiently. In this paper, a method using the geometrical based approach for feature extraction and recognition of six basic emotions has been proposed which is named as GAFCI (Geometrical Approach for Feature Classifier Identification). Various classifiers, Support Vector Machine (SVM), Random Forest, Naïve Bayes and Neural Networks are used for classification, and the performances of all the chosen classifiers are compared. Out of the 83 feature points provided in the BU4DFE database, optimum feature points are identified by experimenting with several sets of feature points. Suitable "feature-classifier" combination has been obtained by varying the number of feature points, classifier parameters, and training and test samples. A detailed analysis on the feature points and classifiers has been performed to learn the relationship between distance parameters and classification of emotions. The results are compared with literature and found to be encouraging.

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“…There are several approaches taken in the literature for learning classifiers for emotion recognition [2] [6].…”

Section: Related Workmentioning

confidence: 99%

“…Computers are "emotionally challenged". They neither recognize other emotions nor possess its own emotion [2].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification Using Facial Expression

Arumugam¹

2011

IJACSA

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Abstract-Human emotional facial expressions play an important role in interpersonal relations. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of emotional state, machines must be taught to understand facial gestures. In this paper we developed an algorithm which is used to identify the person's emotional state through facial expression such as angry, disgust, happy. This can be done with different age group of people with different situation. We Used a Radial Basis Function network (RBFN) for classification and Fisher's Linear Discriminant (FLD), Singular Value Decomposition (SVD) for feature selection.

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Emotion recognition using a Cauchy Naive Bayes classifier

Cited by 82 publications

References 8 publications

GSR and Blink Features for Cognitive Load Classification

GSR and Blink Features for Cognitive Load Classification

Geometrical Approach for Emotion Recognition from Facial Expressions Using 4D Videos and Analysis on Feature-Classifier Combination

Emotion Classification Using Facial Expression

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