Facial Expression Classification With Haar Features, Geometric Features and Cubic Bézier Curves
Year: 2013
Type: article
Abstract: Facial expressions are nonverbal communication channels to interact with other people. Computer recognition of human emotions based on facial expression is an interesting and difficult problem. In this study, images were analyzed based on facial expressions and tried to identify different emotions, such as smile, surprise, sadness, fear, disgust, anger and neutral. In practice, it was used Viola-Jones face detector used AdaBoost algorithm for finding the location of the face. Haar filters were used in finding the eyes and mouth. In cases where erroneous detection of the mouth and eyes, facial geometric ratios were used. Cubic Bezier curves were used in determining emotion. FEEDTUM facial expression database were used for training and testing. The seven different emotions used for the study, the recognition success rates ranged from 97% to 60%.
Source: DergiPark (Istanbul University)
Authors: Rembiye Kandemir, Gonca Özmen
Institutions: Trakya University, Kırklareli University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1639184553