Facial Expression Recognition Using Independent Component Features and Hidden Markov Model

Uddin, Md. Zia; Kim, Tae‐Seong

doi:10.1002/9781118910566.ch5

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Although many advanced methods of using Convolutional Neural Networks (CNN) have been developed, this paper attempts to find the simplest possible technique of classifying emotions, as a process of uncovering the fundamentals of emotion recognition. Emotion recognition techniques [1] [2] generally utilize concepts of optical flow, flow vectors, principal component analysis (PCA), hidden Markov models (HMM) etcetera to recognize emotions. However, the realities of how the human brain processes emotion recognition, appears to stem from a far more fundamental process that accounts for more information processing and understanding than a mere interpretation of facial features.…”

Section: Introductionmentioning

confidence: 99%

A simple multi-feature classification to recognize human emotions in Images

Ipe¹

2020

Preprint

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The recognition of emotions via facial expressions is a complex process of piecing together various aspects of each facial feature. Since viewing a single facial feature in isolation may result in an inaccurate recognition of emotion, this paper attempts training neural networks to first identify specific facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The technique presented is very basic, and can definitely be improved with more advanced techniques that incorporate time<br>and context.

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Section: Introductionmentioning

confidence: 99%

A simple multi-feature classification to recognize human emotions in Images

Ipe¹

2020

Preprint

View full text Add to dashboard Cite

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“…Although many advanced methods of using Convolutional Neural Networks (CNN) have been developed, this paper attempts to go back to the basics to find the simplest possible technique of classifying emotions, as a process of examining the fundamentals of emotion recognition. Emotion recognition techniques [1] [2] generally utilize concepts of optical flow, flow vectors, principal component analysis (PCA), hidden Markov models (HMM) etcetera to recognize emotions. However, the realities of how the human brain processes emotion recognition, appears to stem from a far more fundamental process that accounts for context and associativity to one's own capabilities to form expressions, than a mere interpretation of facial features.…”

Section: Introductionmentioning

confidence: 99%

A simple multi-feature classification to recognize human emotions in Images

Ipe¹

2020

Preprint

View full text Add to dashboard Cite

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific<br>facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.<br>

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Facial Expression Recognition Using Independent Component Features and Hidden Markov Model

Cited by 2 publications

References 21 publications

A simple multi-feature classification to recognize human emotions in Images

A simple multi-feature classification to recognize human emotions in Images

A simple multi-feature classification to recognize human emotions in Images

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