Comparative Analysis of Face Emotion Detection based on Deep Learning Techniques

This research paper presents a comparative analysis of emotion recognition using deep learning techniques. The aim of the study was to compare the performance of state-of-the-art deep learning models, namely Convolutional Neural Network (CNN), Mobilenet and Long Short-Term Memory (LSTM). The dataset used for our work is the popular FER - 2013 dataset, which consist of annotated tweets with emotion labels. The proposed work evaluates the models based on their precision, accuracy, recall, and F1-score. The results show that the CNN model, which was trained using the image dataset, outperforms the other models in terms of precision and F1-score. The study also analyzes the effect of various pre-processing techniques on the performance of the models. Overall, the study provides a comprehensive analysis of emotion recognition using deep learning models and highlights the strengths and weaknesses of different approaches. The results of this study are useful for researchers and practitioners working in the fields of natural language processing and emotion recognition.