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face-emotion-recognition

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A real-time facial emotion recognition system using DenseNet121 CNN, detecting emotions like happy, sad, angry, and surprise from webcam or image input. The system maps emotions to emojis, enhancing interaction. Built with TensorFlow, Keras, and OpenCV, this project showcases deep learning in human-computer interaction.

  • Updated Apr 16, 2025
  • Jupyter Notebook

Face emotion recognition technology detects emotions and mood patterns invoked in human faces. This technology is used as a sentiment analysis tool to identify the six universal expressions, namely, happiness, sadness, anger, surprise, fear and disgust. Identifying facial expressions has a wide range of applications in human social interaction d…

  • Updated Oct 7, 2022
  • Jupyter Notebook

Facial Expression Recognition System using YOLOv9 & Flask. Detects 5 emotions (Angry, Happy, Natural, Sad, Surprised) from images/live camera with mAP50 of 0.731. Features a web interface with file uploads, real-time processing, & emoji feedback. Built with Python, OpenCV, Flask, HTML/CSS/JS. Ideal for HCI & emotion analysis.

  • Updated Apr 16, 2025
  • Jupyter Notebook

Facial Expression Recognition System using YOLOv9 & Flask. Detects 5 emotions (Angry, Happy, Natural, Sad, Surprised) from images/live camera with mAP50 of 0.731. Features a web interface with file uploads, real-time processing, & emoji feedback. Built with Python, OpenCV, Flask, HTML/CSS/JS. Ideal for HCI & emotion analysis.

  • Updated Nov 10, 2025
  • Jupyter Notebook

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