Empirical Study of Human Facial Emotion Recognition: A Deep Learning

A popular method for identifying human expressions is facial emotion recognition. Facial emotion recognition identifies the non-verbal actions of humans. A more exhaustive and trustworthy comprehension of the unique features of classifiers is revealed for researchers by the analysis of classifiers gathered from recent publications. The pre-defined expressions like fear can be identified. An integrated field of research on the capability of computers to perceive and comprehend sentimental situations of human emotions. This paper explains the survey of facial emotions by using feature extraction, classification of images, and data pre-processing. Based on geometry and appearance, there are two widely used approaches that are frequently utilized within the written works for automatic facial emotion recognition systems. Even though static images have been the subject of a lot of research, work is currently being done to create new techniques that would be much simpler to compute and would use less memory than older ones. The results of various facial recognition techniques are compared based on the different algorithms. The datasets covered in this research include CK+, JAFFE, and Emotions with NLP dataset and a few more. We anticipate that the emotion detection will be helpful in various applications, including student predictive learning, and among others. In this survey paper CNN-LSTM based neural network was proposed to improve the accuracy. Emotions with NLP dataset is used. The LSTM model has the capability of using the data in series. We had got the accuracy of 98% by using CNN-LSTM.