Using Bezier Curve analysis in context of Expression Analysis

Modeste, Previste Using Bezier Curve analysis in context of Expression Analysis., 2019 . In ICASSP. (In Press) [Conference paper]

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English abstract

Affective computing is an area of research under increasing demand in the field of computer vision. Expression analysis, in particular, is a topic that has been undergoing research for many years. In this paper, an algorithm for expression determination and analysis is performed for the detection of seven expressions: sadness, anger, happiness, neutral, fear, disgust and surprise. First, the 68 landmarks of the face are detected and the face is realigned and warped to obtain a new image. Next, feature extraction is performed using LPQ. We then use a dimensionality reduction algorithm followed by a dual RBF-SVM and Adaboost classification algorithm to find the interest points in the features extracted. We then plot bezier curves on the regions of interest obtained. The curves are then used as the input to a CNN and this determines the facial expression. The results showed the algorithm to be extremely successful

Item type:	Conference paper
Keywords:	Bezier, Expression analysis
Subjects:	L. Information technology and library technology > LP. Intelligent agents.
Depositing user:	Mr Previte Modeste
Date deposited:	10 May 2020 10:14
Last modified:	10 May 2020 10:14
URI:	http://hdl.handle.net/10760/39933

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