A Continuous Facial Expression Recognition Model based on Deep Learning Method Effective facial expression recognition is a very important part of perceiving the useru0027s emotions and designing a successful humancomputer interaction system in all fields. Many studies also applied computer vision, machine learning, and deep learning methods on affective computing. However, they mainly focus on the facial expression identification by a single static image, but didnu0027t consider the continuous facial expression of human emotions maybe is for a period of times. Therefore, to recognize the pattern of continuous facial expression and mood changes for further application is still a challenging task. In this study, we propose a continuous facial expression recognition model based on deep learning approach, which combined convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze and identify continuous facial expressions in a period of time to improve the traditional image recognition method.

Deep Learning for Face Expressions Detection: Enhanced Recurrent Neural Network with Long Short Term Memory In recent years, deep learning neural frameworks have been given significant attention in programming development, especially in machine learning, machine vision and artificial intelligence (AI). The ability to detect faces has inspired many researchers because a human face shows great dissimilarities in form and figure due to changes in position and expression in different situations. This research aims to produce a method of applying recurrent neural network (RNN) designs using long shortterm memory (LSTM) to identify facial expressions. The proposed method involves an improved RNN that uses LSTM to increase the effectiveness of the feature extraction process using input sets which regenerate the inputdata from the features. The accuracy and computing time of this technique were studied. With LSTMRNNs, the results show that the design gives enhanced outcomes compared with other methods, including most imagevideo face detection methods. The efficiency evaluation of LSTMRNNs in images and in video frame series shows that there are performance improvements of more than 5% compared with traditional neural networks.