International Society of Science and Applied Technologies |
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Robust Facial Expression Recognition via Principal Component Analysis and AdaBoost Algorithm | ||||
Author | Cheng Fan
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Co-Author(s) | Eric T.T. Wong
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Abstract | Facial expression is a key component in evaluating a person’s feelings, intentions and characteristics. Facial expression is an important part of human-computer interaction and has the potential to play an equal important role in real life applications. This paper is aimed to propose a robust approach for facial expression recognition through Principal Component Analysis (PCA) and AbaBoost algorithm. With the help of PCA one can extract the important feature pixels in a facial expression figure for the feature classifier to recognize the facial expression concerned. We propose to combine the AbaBoost algorithm with a special machine learning algorithm to reduce the error rate in feature classification. The proposed approach was applied to images of facial expressions posed by a Japanese female model in The Japanese Female Facial Expression (JAFEE) database. The classifying precision 92.45% has demonstrated the feasibility of our proposal method.
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Keywords | Facial expression recognition, AdaBoost, principal component analysis | |||
Article #: 24255 |
August 2-4, 2018 - Toronto, Ontario, Canada |