Facial Emotion Recognition System Using CNN for Song Mapping

Authors

  • Narayan Paudel Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Saugat Neupane Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Smarika Shrestha Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Suyasha Nepal epartment of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Pralhad Chapagain Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal

DOI:

https://doi.org/10.3126/injet.v1i2.66709

Abstract

This project introduces a novel system that integrates Facial Emotion Recognition (FER) with music mapping to enhance human-computer interaction. Leveraging a custom Convolutional Neural Network (CNN) algorithm, developed using the FER2013 dataset, we classify emotions into four categories- happy, sad, neutral, and angry- employing Haar Cascade for precise face detection and grayscale conversion for optimal CNN input. Our custom CNN demonstrates superior performance, achieving a testing accuracy of 77.23%, notably surpassing established models like VGG16 and ResNet50, which achieved 55.87% and 62.76% respectively. This system swiftly identifies emotions and recommends songs from the "278k Emotion Labeled Spotify Songs" playlist, aiming to boost user satisfaction. Through nuanced comparisons with VGG16 and ResNet50, our approach underscores its inherent strengths, suggesting promising advancements in Facial Emotion Recognition (FER) and music recommendation systems. With a focus on precision in emotion detection, our system subtly elevates user experience, contributing meaningfully to ongoing research in the field.

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Published

2024-06-24

How to Cite

Paudel, N., Neupane, S., Shrestha, S., Nepal, S., & Chapagain, P. (2024). Facial Emotion Recognition System Using CNN for Song Mapping. International Journal on Engineering Technology, 1(2), 312–323. https://doi.org/10.3126/injet.v1i2.66709

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Section

Articles