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: 25 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 (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-03-08T20:04:21.000Z (8 months ago)
• Last Synced: 2024-10-01T17:42:10.534Z (about 1 month ago)
• Language: Python
• Homepage:
• Size: 118 MB
• Stars: 5,594
• Watchers: 227
• Forks: 1,591
• Open Issues: 54
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-lists - face_classification
• awesome-affective-computing - face_classification - time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV. (Behavioral)
• awesome-cv - face_classification - time face detection and emotion/gender classification | (Repos / Tags: Object Classification `[ObjCls]`, Object Detection `[ObjDet]`, Object Segmentation `[ObjSeg]`, General Library `[GenLib]`, Text Reading / Object Character Recognition `[OCR]`, Action Recognition `[ActRec]`, Object Tracking `[ObjTrk]`, Data Augmentation `[DatAug]`, Simultaneous Localization and Mapping `[SLAM]`, Outlier/Anomaly/Novelty Detection `[NvlDet]`, Content-based Image Retrieval `[CBIR]`, Image Enhancement `[ImgEnh]`, Aesthetic Assessment `[AesAss]`, Explainable Artificial Intelligence `[XAI]`, Text-to-Image Generation `[TexImg]`, Pose Estimation `[PosEst]`, Video Matting `[VidMat]`, Eye Tracking `[EyeTrk]`)
• awesome-affective-computing - face_classification - time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV. (Related / Video-games)
• awesome-list - face_classification - Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV. (Computer Vision / Classification & Detection & Tracking)

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:

![alt tag](images/demo_results.png)

Guided back-prop

![alt tag](images/gradcam_results.png)

Real-time demo:



  



[B-IT-BOTS](https://mas-group.inf.h-brs.de/?page_id=622) robotics team :)

![alt tag](images/robocup_team.png)

## 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