Emotion-recognition and emotion-recognition-neural-networks
These tools are competitors, as both repositories provide real-time facial emotion recognition using deep neural networks, with one leveraging a general real-time approach and the other specifically mentioning TensorFlow and DNNs.
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-recognition-neural-networks
isseu/emotion-recognition-neural-networks
Emotion recognition using DNN with tensorflow
**Technical Summary:** Implements facial emotion classification across seven expressions (angry, disgusted, fearful, happy, sad, surprised, neutral) using convolutional neural networks on the FER-2013 dataset. The project provides multiple CNN architectures beyond the default AlexNet, with preprocessing pipelines that convert raw CSV image data to NumPy arrays for training. Includes both offline training and real-time inference modes via webcam integration.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work