A Combined Approach for Emotion Recognition using Bezier Curve and Facial Expression Analysis

Extracting and comprehension of emotion is of high significance for the collaboration
among human and machine communication frameworks. The most expressive
approach to show the human’s emotion is through facial expression analysis. Facial
Expression is an acknowledged, non-nosy additionally skillful technique for
communication that has been well thoroughly considered as a plausible cooperation
of such interface. This paper introduces and actualizes an extraction and
acknowledgment technique for facial expression and emotion from still picture. The
mean of this examination is making a Facial Expression Recognition (FER) conspire
by utilizing Bezier curve and afterward checking the precision by utilizing diverse
classifiers (Naïve Bayes, SVM and ANN). After taking a picture from dataset and
sequentially applying skin color segmentation, largest connected component and
binary image conversion, eyes and lips are isolated from the face. Subsequently
Bezier curve for eyes and lips are recognized and are contrasted with those pictures
that are stored in the database. Next it finds the nearest Bezier curve from the
database. In this way by utilizing these technique emotions (Smile, Normal, Sad &
Surprise) communicated by the human face can be distinguished effortlessly. Hence,
a Bezier curve based solution together with image processing is used in classifying
the emotions by using WEKA. Finally, ANN, Naïve Bayes and SVM classifiers are
used to determine the accuracy of the system by training and testing the dataset.

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