Analysis of Face Emotion Identification and Recognition Using State-of-the-Art Deep Learning Models

The application of several DL approaches for FER has been a focus of many researchers worldwide.Many Deep Learning algorithms are available to do this task, but the absence of large online datasets prevents any of them from achieving better accuracy.Researchers have decided to transfer learning as a solution to this problem after increasing prediction accuracy.This research presents a comparative examination of different DL pre-trained models for face emotion identification that may be applied in different real-world scenarios.Using deep learning approaches, this research endeavour gives a comparative assessment of recognising emotions of faces.The study aims to compare and assess the performance of two advanced DL models: transfer learning pre-trained models and CNNs.The well-known FER-2013 dataset, which consists of various emotion classes for classification, was utilised for this study.Phases of automated emotion identification, including data preprocessing, categorization, and visualisation, are presented.The models were assessed in the experimental work according to their F1-score, recall, accuracy, and precision.The CNN model gets 75.47% accuracy for the facial expression recognition, which outperforms other transfer learning models.The report summarises previous research on emotion recognition with deep learning models and compares and contrasts various methods.The study's results can help people who study and work in areas of data processing, DL, and facial emotion detection.