An Automatic Facial Expression Recognition System Employing Convolutional Neural Network with Multi-strategy Gravitational Search Algorithm. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Alenazy, Wael Mohammad; Alqahtani, Abdullah Saleh
• Is Part Of: IETE technical review. Volume 39:Issue 1(2022)
• Page Start: 72
• Page End: 85
• Publication Date: 2022-01-02
• Subjects: Facial Expression -- Convolutional Neural Network -- Multi-strategy Gravitational Search Algorithm -- SVM Classifier -- And Hyperparameter Tuning; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; 621.38
• DOI: 10.1080/02564602.2020.1825125
• Languages: English
• ISSNs: 0256-4602

Abstract

Abstract : Facial expression recognition (FER) plays a vital role in image processing according to the widespread development of human interactive applications. In the past few years, various researchers have focused on FER for implementing it in different applications. The existing system suffers from various complexities such as low accuracy, computational cost, and poor recognition performances. In this article, we proposed a novel concept to recognize facial expressions. The proposed work comprises three sections that are pre-processing, feature extraction, and classification. The pre-processing techniques remove the unwanted data from the original image and enhance the crucial details for further processing. The Convolutional Neural Network (CNN) is used for feature extraction. But, it yields lower performance in terms of feature extraction due to the shortage of hyperparameter tuning. Hence, the Multi-strategy Gravitational Search Algorithm (M-GSA) is utilized to extract the facial expression features from the eyebrow movement, nose, chin, and lip corner of the facial images. The facial expressions are classified via the Support Vector Machine (SVM) classifier. In this work, the top five facial expressions such as surprise, sad, happy, fear, and angry with three facial expression datasets such as FER-2013 dataset, CK + dataset, and JAFFE dataset. Ultimately, the proposed method demonstrates better classification accuracy and recognition rates than different kinds of state-of-art methods.