Human facial expressions are an indication of true emotions. To recognize facial expressions accurately is useful in the field of Artificial Intelligence, Computing, Medical, e-Education, and many more. The facial expression recognition (FER) system detects emotion through facial expression. But, it is challenging to detect facial emotions accurately. However, recent advancements in technology, research, and availability of facial expression datasets have led to the development of many FER systems which can accurately detect facial emotions. Past research in the field of FER indicates With Convolutional Neural Networks (CNNs), deep learning techniques are the most advanced presently. Custom CNN Architecture is used to implement basic facial emotion recognition in static images in this paper. A K-fold cross-validation method was used to train them using FER13, CK+, and the JAFFE data set. On the seven classes of fundamental emotions, including anger, disgust, fear, happiness, neutrality, sorrow, and surprise, the FER13, CK+, and JAFFE datasets had an accuracy rate of 91.58 percent. Given the difficulty of developing unique CNN architecture, this study’s accurate findings contrast well with those of previous studies.

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The automatic measurement of pain intensity from facial expressions, mainly from face images describes the patient’s health. Hence, a robust technique, named Water Cycle Henry Gas Solubility Optimization-based Deep Neuro Fuzzy Network (WCHGSO-DNFN) is designed for compound FER and pain intensity measurement. However, the proposed WCHGSO is the incorporation of Water Cycle Algorithm (WCA) with Henry Gas Solubility Optimization (HGSO). Here, Compound Facial Expressions of Emotion Database (dataset-2) is made to perform compound FER, whereas the input image from UNBC pain intensity dataset (dataset-1) is utilized to measure the pain intensity, and the processes are performed separately. The developed technique achieved better performance with respect to testing accuracy, sensitivity, and specificity with the highest values of 0.814, 0.819, and 0.806 using dataset-1, whereas maximum values of 0.815, 0.758 and 0.848 is achieved using dataset-2.

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