Abstract
Facial Expression Recognition (FER) draws much attention in present research discussions. The paper presents a relative analysis of recognition systems for facial expression. Facial expression recognition is generally carried out in three stages such as detection of face, extraction of features and expressions’ classification. The proposed work focuses on a face detection and extraction method is presented based on the Haar cascade features. The classifier is trained by using many positive and negative images. The features are extracted from it. For standard Haar feature images like convolutional kernel are used. Next, Fisher face classifier a supervised learning method is applied on COHN-KANADE database, to have a facial expression classifying system with eight possible classes (seven basic emotions along with neutral). A recognition rate of 65\(\%\) in COHN-KANADE database is achieved.
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Suneeta, V.B., Purushottam, P., Prashantkumar, K., Sachin, S., Supreet, M. (2020). Facial Expression Recognition Using Supervised Learning. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_32
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DOI: https://doi.org/10.1007/978-3-030-37218-7_32
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