Emotion-recognition and Emotion-detection
Both projects are independent implementations of the same core functionality (CNN-based real-time facial emotion classification), making them direct competitors rather than complementary or related tools.
About Emotion-recognition
otaha178/Emotion-recognition
Real time emotion recognition
Leverages convolutional neural networks with a pretrained classifier to detect seven emotion categories from facial features captured via webcam, displaying probability distributions for mixed emotions. Uses the FER2013 dataset (achieving 66% accuracy) and provides both inference and retraining capabilities through Python scripts. Integrates OpenCV for image processing and includes a real-time visualization interface showing emotion probabilities alongside live video feed.
About Emotion-detection
atulapra/Emotion-detection
Real-time Facial Emotion Detection using deep learning
Classifies facial expressions into seven emotion categories (angry, disgusted, fearful, happy, neutral, sad, surprised) using a 4-layer CNN trained on the FER-2013 dataset. The pipeline combines Haar cascade face detection for real-time frame analysis with 48x48 grayscale image normalization before CNN inference, outputting softmax probability scores for each emotion class. Built on TensorFlow 2.0 with Keras API and OpenCV for webcam integration, achieving 63.2% test accuracy.
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