https://github.com/oarriaga/face_classification
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
https://github.com/oarriaga/face_classification
Last synced: 4 days ago
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Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
- Host: GitHub
- URL: https://github.com/oarriaga/face_classification
- Owner: oarriaga
- License: mit
- Created: 2017-02-09T14:44:32.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-03-08T20:04:21.000Z (about 1 year ago)
- Last Synced: 2025-04-03T20:37:40.939Z (11 days ago)
- Language: Python
- Homepage:
- Size: 118 MB
- Stars: 5,653
- Watchers: 228
- Forks: 1,600
- Open Issues: 56
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# This repository is deprecated for at TF-2.0 rewrite visit:
# https://github.com/oarriaga/paz
------------------------------------------------
# Face classification and detection.
Real-time face detection and emotion/gender classification using fer2013/IMDB datasets with a keras CNN model and openCV.
* IMDB gender classification test accuracy: 96%.
* fer2013 emotion classification test accuracy: 66%.
For more information please consult the [publication](https://github.com/oarriaga/face_classification/blob/master/report.pdf)
# Emotion/gender examples:
Guided back-prop
Real-time demo:
[B-IT-BOTS](https://mas-group.inf.h-brs.de/?page_id=622) robotics team :)
## Instructions
### Run real-time emotion demo:
> python3 video_emotion_color_demo.py
### Run real-time guided back-prop demo:
> python3 image_gradcam_demo.py
### Make inference on single images:
> python3 image_emotion_gender_demo.py
e.g.
> python3 image_emotion_gender_demo.py ../images/test_image.jpg
### Running with Docker
With a few steps one can get its own face classification and detection running. Follow the commands below:
* ```docker pull ekholabs/face-classifier```
* ```docker run -d -p 8084:8084 --name=face-classifier ekholabs/face-classifier```
* ```curl -v -F image=@[path_to_image] http://localhost:8084/classifyImage > image.png```
### To train previous/new models for emotion classification:
* Download the fer2013.tar.gz file from [here](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)
* Move the downloaded file to the datasets directory inside this repository.
* Untar the file:
> tar -xzf fer2013.tar
* Run the train_emotion_classification.py file
> python3 train_emotion_classifier.py
### To train previous/new models for gender classification:
* Download the imdb_crop.tar file from [here](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) (It's the 7GB button with the tittle Download faces only).
* Move the downloaded file to the datasets directory inside this repository.
* Untar the file:
> tar -xfv imdb_crop.tar
* Run the train_gender_classification.py file
> python3 train_gender_classifier.py