Facial Emotion-Based Music Player

Abstract

Artificial Intelligence has applied in many significant fields in which expression and emotion detection is one of them. To detect a facial expression, the system should analyze various variability of human faces like colour, posture, expression, orientation, lighting, etc. Detecting facial features is a prerequisite to facial emotion recognition. One of the applications of this input can be for extracting the information to deduce the mood of an individual. This data can then be used to get a list of songs that comply with the “mood” derived from the input provided earlier. This eliminates the time-consuming and tedious task of manually segregating or grouping songs into different lists and helps in generating an appropriate playlist based on an individual's emotional features. Facial Expression Based Music System aims at scanning and interpreting the data and accordingly creating a playlist based on the parameters provided. Thus our proposed system focuses on detecting human emotions for developing emotion-based music players. This is achieved by observing the parts of the face, like eyes, lips movement, etc. These are then classified and compared to trained sets of data. In this research, a human facial expression recognition system will be modelled using the eigenface approach. The proposed method will use the HAAR Cascade classifier to detect the face in an image. Fisher Faces calculation can be utilized for decreasing the high dimensionality of the Eigen space and after that anticipating the test picture upon the Eigen space and computing the Euclidean separation between the test picture and meaning of the Eigen faces. The grayscale image of the face is used by the system to classify five basic emotions such as surprise, disgust, neutral, anger, and happiness.