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This project is a real-time facial emotion recognition program that uses a webcam feed and a MobileNetV2 based classifier to detect and classify human emotions. It identifies faces in each video frame using OpenCV’s HaarCascade Classifier, processes the detected face, and predicts the emotion Angry, Happy, Neutral, Sad, or Surprised, using a MobileNetV2 model trained on a customized version of the FER-2013 dataset! Depending on the detected emotion, the program displays a corresponding meme hamster (or hampter lol) as you scroll down on the site! The project runs in Python 3.8+ and deployed through streamlit. This project has been one of the most FRUSTRATING but albeit incredible learning experiences ever. Ive been reading books on tensorflow and working with streamlit was interesting as well. I had previously tried to make a blog using streamlit but only got halfway through and dropped it, so this was a really fun opportunity to actually use it in a way more diverse and fun way and improve on the little bit of knowledge I already had of it before :D
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by @cloudyDays-mThis project is a real-time facial emotion recognition program that uses a webcam feed and a MobileNetV2 based classifier to detect and classify human emotions. It identifies faces in each video frame using OpenCV’s HaarCascade Classifier, processes the detected face, and predicts the emotion Angry, Happy, Neutral, Sad, or Surprised, using a MobileNetV2 model trained on a customized version of the FER-2013 dataset! Depending on the detected emotion, the program displays a corresponding meme hamster (or hampter lol) as you scroll down on the site! The project runs in Python 3.8+ and deployed through streamlit. This project has been one of the most FRUSTRATING but albeit incredible learning experiences ever. Ive been reading books on tensorflow and working with streamlit was interesting as well. I had previously tried to make a blog using streamlit but only got halfway through and dropped it, so this was a really fun opportunity to actually use it in a way more diverse and fun way and improve on the little bit of knowledge I already had of it before :D