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.

Download: PDF

Submission history

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

Unique-IP document downloads: 6 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus