Title

Technical Papers Session IV: A new perspective towards analysis of human facial expression using supervised classification algorithms

Abstract/Description

As cost-effective and relatively accurate models for behavior classification, automatic facial expression analysis has the potential to be applied to multiple disciplines. The current research deals with real-time classification of evoked emotions in children. Reason behind analyzing children behavior is that they possess immature cognitive abilities compared to adults, they are more likely to be influenced by external stimuli and they are less likely to pose and hide expression. This research work uses a three-phase classification model to analyze real time captured emotion varying children facial expressions. Two classifiers K-Nearest Neighbor and SVM were used for classification. The classification model was tested using our own real time recorded children Facial Expression (CFE).

Location

Lecture Hall A (Aman Tower, 12th floor)

Session Theme

Technical Papers Session IV - Artificial Intelligence

Session Type

Parallel Technical Session

Session Chair

Engr. Parkash Lohana

Start Date

17-11-2019 2:20 PM

End Date

17-11-2019 2:40 PM

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Nov 17th, 2:20 PM Nov 17th, 2:40 PM

Technical Papers Session IV: A new perspective towards analysis of human facial expression using supervised classification algorithms

Lecture Hall A (Aman Tower, 12th floor)

As cost-effective and relatively accurate models for behavior classification, automatic facial expression analysis has the potential to be applied to multiple disciplines. The current research deals with real-time classification of evoked emotions in children. Reason behind analyzing children behavior is that they possess immature cognitive abilities compared to adults, they are more likely to be influenced by external stimuli and they are less likely to pose and hide expression. This research work uses a three-phase classification model to analyze real time captured emotion varying children facial expressions. Two classifiers K-Nearest Neighbor and SVM were used for classification. The classification model was tested using our own real time recorded children Facial Expression (CFE).