Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Real-time Facial Emotion Detection using deep learning
| Date | Stars |
|---|---|
| 2026-07-24 | 1358 |
| 2026-07-25 | 1358 |
| 2026-07-28 | 1360 |
| 2026-07-30 | 1360 |
| 2026-07-31 | 1361 |
| 2026-08-06 | 1357 |
| 2026-08-07 | 1358 |
| 2026-08-10 | 1358 |
| 2026-08-11 | 1359 |
| 2026-08-12 | 1358 |
| 2026-08-18 | 1358 |
| 2026-08-20 | 1359 |
| 2026-09-07 | 1358 |
| 2026-09-10 | 1359 |
| 2026-09-12 | 1359 |
| 2026-09-19 | 1359 |
| 2026-09-20 | 1359 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Emotion detection using deep learning ## Introduction This project aims to classify the emotion on a person's face into one of **seven categories**, using deep convolutional neural networks. The model is trained on the **FER-2013** dataset which was published on International Conference on Machine Learning (ICML). This dataset consists of 35887 grayscale, 48x48 sized face images with **seven emotions** - angry, disgusted, fearful, happy, neutral, sad and surprised. ## Dependencies * Python 3, [OpenCV](https://opencv.org/), [Tensorflow](https://www.tensorflow.org/) * To install the required packages, run `pip install -r requirements.txt`. ## Basic Usage The repository is currently compatible with `tensorflow-2.0` and makes use of the Keras API using the `tensorflow.keras` library. * First, clone the repository and enter the folder ```bash git clone https://github.com/atulapra/Emotion-detection.git cd Emotion-detection ``` * Download the FER-2013 dataset inside the `src` folder. * If you want to train this model, use: ```bash cd src python emotions.py --mode train ``` * If you want to view the predictions without training again, you can download the pre-trained model from [here](https://drive.google.com/file/d/1FUn0XNOzf-nQV7QjbBPA6-8GLoHNNgv-/view?usp=sharing) and then run: ```bash cd src python emotions.py --mode display ``` * The folder structure is of the form: src: * data (folder) * `emotions.py` (file) * `haarcascade_frontalface_default.xml` (file) * `model.h5` (file) * This implementation by default detects emotions on all faces in the webcam feed. With a simple 4-layer CNN, the test accuracy reached 63.2% in 50 epochs.  ## Data Preparation (optional) * The [original FER2013 dataset in Kaggle](https://www.kaggle.com/deadskull7/fer2013) is available as a single csv file. I had converted into a dataset of images in the PNG format for training/testing. * In case you are looking to experiment with new datasets, you may have to deal with data in the csv format. I have provided the code I wrote for data preprocessing in the `dataset_prepare.py` file which can be used for reference. ## Algorithm * First, the **haar cascade** method is used to detect faces in each frame of the webcam feed. * The region of image containing the face is resized to **48x48** and is passed as input to the CNN. * The network outputs a list of **softmax scores** for the seven classes of emotions. * The emotion with maximum score is displayed on the screen. ## References * "Challenges in Representation Learning: A report on three machine learning contests." I Goodfellow, D Erhan, PL Carrier, A Courville, M Mirza, B Hamner, W Cukierski, Y Tang, DH Lee, Y Zhou, C Ramaiah, F Feng, R Li, X Wang, D Athanasakis, J Shawe-Taylor, M Milakov, J Park, R Ionescu, M Popescu, C Grozea, J Bergstra, J Xie, L Romaszko, B Xu, Z Chuang, and Y. Bengio. arXiv 2013.
Excerpt of 2,962 characters
Read on GitHub
58
9
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2ef693f4bd065cab, topic:computer-vision, topic:opencv
matched fp:2ef693f4bd065cab, topic:deep-learning