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Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier

Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier

Manoj Prabhakaran Kumar, Manoj Kumar Rajagopal
Copyright: © 2021 |Pages: 38
ISBN13: 9781799821083|ISBN10: 1799821080|ISBN13 Softcover: 9781799821090|EISBN13: 9781799821106
DOI: 10.4018/978-1-7998-2108-3.ch003
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MLA

Kumar, Manoj Prabhakaran, and Manoj Kumar Rajagopal. "Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier." Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., IGI Global, 2021, pp. 76-113. https://doi.org/10.4018/978-1-7998-2108-3.ch003

APA

Kumar, M. P. & Rajagopal, M. K. (2021). Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier. In K. Senthilnathan, B. Shanmugam, D. Goyal, I. Annapoorani, & R. Samikannu (Eds.), Deep Learning Applications and Intelligent Decision Making in Engineering (pp. 76-113). IGI Global. https://doi.org/10.4018/978-1-7998-2108-3.ch003

Chicago

Kumar, Manoj Prabhakaran, and Manoj Kumar Rajagopal. "Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier." In Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., 76-113. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2108-3.ch003

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Abstract

This chapter proposes the facial expression system with the entire facial feature of geometric deformable model and classifier in order to analyze the set of prototype expressions from frontal macro facial expression. In the training phase, the face detection and tracking are carried out by constrained local model (CLM) on a standardized database. Using the CLM grid node, the entire feature vector displacement is obtained by facial feature extraction, which has 66 feature points. The feature vector displacement is computed in bi-linear support vector machines (SVMs) classifier to evaluate the facial and develops the trained model. Similarly, the testing phase is carried out and the outcome is equated with the trained model for human emotion identifications. Two normalization techniques and hold-out validations are computed in both phases. Through this model, the overall validation performance is higher than existing models.

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