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.

随着人类交互应用的广泛发展，面部表情识别（FER）在图像处理中发挥着至关重要的作用。在过去的几年中，各种研究人员都将注意力集中在 FER 上，以便在不同的应用中实现它。现有系统存在精度低、计算成本低、识别性能差等各种复杂问题。在这篇文章中，我们提出了一个新的概念来识别面部表情。拟议的工作包括三个部分，即预处理、特征提取和分类。预处理技术从原始图像中删除不需要的数据，并增强关键细节以进行进一步处理。卷积神经网络 (CNN) 用于特征提取。但，由于缺乏超参数调整，它在特征提取方面的性能较低。因此，利用多策略引力搜索算法（M-GSA）从面部图像的眉毛运动、鼻子、下巴和唇角中提取面部表情特征。面部表情通过支持向量机（SVM）分类器进行分类。在这项工作中，使用 FER-2013 数据集、CK 等三个面部表情数据集的前五名面部表情，如惊讶、悲伤、快乐、恐惧和愤怒⁺数据集和 JAFFE 数据集。最终，所提出的方法表现出比不同种类的最先进方法更好的分类准确性和识别率。