Human Emotion Detection and Music Player using Machine Learning

Human Emotion Detection and Music Player using Machine Learning

Vaishnavi Borgaonkar, PG Student, Maharashtra Institute of Technology college of engineering, Aurangabad, India,

Prof. Sarita Sanap, Assistant Professor, Maharashtra Institute of Technology college of engineering, Aurangabad, India,

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Abstract - The model presents the design, implementation, test, and evaluation of a Facial Expression Recognition (FER) and automatic music recommendation system that applies a machine learning algorithm based on Convolutional Neural Networks (CNNs) with the aim of correctly classifying four facial expressions (namely happiness, sadness, anger, and neutral). The CNN module and the FER system were built in python.

Facial expressions convey helpful information that is difficult to detect for an ordinary system. However, being capable of recognizing them could lead to more responsive and intelligent systems that might improve the user experience. By experimenting with Fisherface model and proposed CNN model on facial Cohn-Kanade dataset, the most suitable hyper parameters that yielded a good level of performance were obtained. Additionally, a deep understanding of the strengths and limitations of CNNs was gained.

Results from these experiments show that special care must be taken during different parts of the development process such as architecture selection or hyper parameter tuning. By selecting the correct combination between these two elements, the accuracy of the model and the convergence time improve. The training accuracy for the proposed CNN model is 72.34% which is very similar to the pretrained AlexNet CNN model which is 72.77%. In a Real-Time testing Fisherface  is used to detect the face for further facial expression classification