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https://github.com/atulapra/Emotion-detection
Real-time Facial Emotion Detection using deep learning
https://github.com/atulapra/Emotion-detection
computer-vision deep-learning emotion-detection emotion-recognition haar-cascade opencv opencv-python tflearn
Last synced: 4 months ago
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Real-time Facial Emotion Detection using deep learning
- Host: GitHub
- URL: https://github.com/atulapra/Emotion-detection
- Owner: atulapra
- License: mit
- Created: 2017-08-06T06:28:56.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2023-03-31T17:08:20.000Z (over 1 year ago)
- Last Synced: 2024-01-18T08:24:38.532Z (6 months ago)
- Topics: computer-vision, deep-learning, emotion-detection, emotion-recognition, haar-cascade, opencv, opencv-python, tflearn
- Language: Python
- Homepage:
- Size: 15.7 MB
- Stars: 1,035
- Watchers: 30
- Forks: 441
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - atulapra/Emotion-detection - Real-time Facial Emotion Detection using deep learning (Python)
README
# 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.