A survey of advancements in Face Emotion Recognition using Machine and Deep Learning algorithms

In recent times, face recognition is a kind of biometric authentication which is widely used for security purpose in many areas like image processing, pattern recognition. However, recognizing the human face was very difficult tasks such as data quality, overfitting problems, privacy concerns and variability in expressions. Hence, to address these issues in face emotion recognition, this survey utilized various classification and convolutional models to improve the performances. The advantage of using the recommended method is it will minimize the training process time by utilizing conventional models and classifiers to improve the performance effectively. Initially, Machine Learning (ML) algorithms utilizing classifiers to improve the convergence speed, accuracy and efficiency. Next, Deep Learning (DL) algorithms utilizing conventional models to improve the functionality, Generalization and enhanced performances. Furthermore, this survey addresses the advantages and disadvantages of different models for facial emotion recognition, thus facilitating the selection of suitable algorithms.