Human Emotion Recognition and Song Recommendation Model
DOI:
https://doi.org/10.58445/rars.1046Keywords:
computer science, machine learning, emotion recognition, song recommendationAbstract
In this paper, I present a project that recognizes human emotions and recommends a song based on them. This model uses machine learning techniques to identify the five basic human emotions from facial images: happiness, anger, sadness, surprise, and neutral. This model employs a single machine learning model, which is trained on a dataset of labeled facial images and uses Haar Cascade classifier and Local Binary Patterns Histograms algorithm (for feature extraction and classification) to recognize emotions in uploaded images. A supporting code is used to recommend songs based on the recognized emotion. This paper evaluates the accuracy of the human emotion recognition model and the effectiveness of the song recommendation system. The findings of this study can contribute to the development of more advanced emotion recognition models and music recommendation systems in the future.
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