A Robust Emotion Detection and Music Recommendation System using Mini Xception CNN

Abstract

Abstract: Due to the emerging developments in artificial intelligence(AI) and machine learning(ML) Technology various systems are developed in recent days that late the human emotions and real time aspects of human psychology detection. Facial recognition based music recommendation system (MRS) is a interesting area of research where he plays an important role in handling the psychology patients. Face recognition system is extensively applied in security systems surveillance systems fault identification etc. Based on Emotion of the human, the music recommendation needs to be provided to analyse the phycology changes with the patients. The proposed approach is focused on considering the constraints available with the facial recognition system in existing frameworks such as deep feature extraction processing delay need to be reduce the hair using deep convolutional neural network(DCNN) architecture based Mini exception algorithm is developed. The system considered FER2013 image dataset that contains 35000 face images with automated labels that would be helpful for the presented approach to identify the emotion class accurately. The Mini exception algorithm used in CNN layers act as a lightweight system compared with various states of approaches. The proposed system removes the barrier between the existing frameworks and achieved the accuracy of 92%. The recommended music is derived from the music database and further mapped with respect to the algorithm result.