Facial Expression Recognition using Histogram of Oriented Gradients with SVM-RFE Selected Features


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Saurav , S and Singh, S and Saini, R (2019) Facial Expression Recognition using Histogram of Oriented Gradients with SVM-RFE Selected Features. In: 19th International Conference on Hybrid Intelligent Systems, December 10-12, 2019, VIT, Bhopal, India.

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This study is an attempt towards improving the accuracy and execution time of a facial expression recognition (FER) system. The algorithmic pipeline consists of a face detector block, followed by a facial alignment and registration, feature extraction, feature selection, and classification blocks. The proposed method utilizes histograms of oriented gradients (HOG) descriptor to extract features from expressive facial images. Support vector machine recursive feature elimination (SVM-RFE), a powerful feature selection algorithm is applied to select the most discriminant features from high-dimensional feature space. Finally, the selected features were fed to a support vector machine (SVM) classifier to determine the underlying emotions from expressive facial images. Performance of the proposed approach is validated on three publicly available FER databases namely CK+, JAFFE, and RFD using different performance metrics like recognition accuracy, precision, recall, and Fl-Score. The experimental results demonstrated the effectiveness of the proposed approach in terms of both recognition accuracy and execution time.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Facial expression recognition (FER), Histogram of oriented gradients (HOG), Feature selection, Support vector machine (SVM) classifier.
Subjects: Semiconductor Devices > Hybrid Microcircuits
Divisions: Semiconductor Devices
Depositing User: Mr. Jitendra Nath Bajpai
Date Deposited: 10 Sep 2021 11:31
Last Modified: 10 Sep 2021 11:31
URI: http://ceeri.csircentral.net/id/eprint/529

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