Artificial Intelligence


Facial Expression Analysis by K-Means Clustering on Fiducial Points of Face

Authors: Previte Modesto, Saul Reitano

Human beings produce thousands of facial actions and emotions in a single day. These come up while communicating with someone and at times even when alone. These expressions vary in complexity, intensity, and meaning. This paper proposes a novel method to predict what emotion is being expressed by analyzing the face. The algorithm, because of the speed of execution, could also be used for micro expression analysis. 11 fiducial points are taken on the image after a face recognition algorithm is used. 7 classes of images are formed. These classes are the main expressions: sadness, happiness, anger, fear, disgust, surprise and neutral. Training is done by studying the relationship between the fiducial points for each class of image. Using this relationship a new image is classified by making use of the k-means algorithm.

Comments: 5 Pages.

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Submission history

[v1] 2019-10-29 10:51:31

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