A Deep Learning approach for Emotion Based Music Player

Deep Learning mechanisms can be leveraged for playing the type of music based on the emotions of an individual entity. This can be done by detecting the human facial expressions, color, posture, orientation, lightning, etc. An interface is designed which makes the system to analyze the possible variability of faces. The basic pre-requisite for emotion recognition is appropriate selection of facial features that helps in identifying the mood of a person. Traditionally, grouping songs into various playlist was manual interpreted that consumed lot of time and it was indeed a tedious task. However, the advent of Facial Expression Based Music System emphasizes an automatic creation of music playlist based on real time mental state of an individual. In this work we have employed Haar Cascade-CNN classifier and SVM classifier to detect the emotions in an image. Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. The learning algorithm keeps on training the input feature vector based on the image captured. The gray scale image of the face is used by the system to classify five basic emotions such as surprise, disgust, neutral, anger and happiness. The emotion classification is achieved by observing the parts of the face, like eyes, lips movement, etc. A comparative study of these two classifiers are conducted based on the trained datasets. This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.