This Python application leverages the power of DeepFace, a deep learning library, to perform multiple tasks related to facial analysis. With this app, you can upload an image, and it will:
- Detect Faces: Automatically identify and locate faces within the image.
- Emotion Recognition: Determine the dominant emotion expressed by the detected face(s).
- Age Prediction: Estimate the age of the person(s) in the image.
- Gender Identification: Recognize the gender of the person(s) in the image.
- Race Detection: Identify the dominant racial attributes of the person(s) in the image.
faceapp
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data <- Images that represent emotions.
│ ├── angry.jpg
│ ├── disgust.jpg
│ ├── fear.jpg
│ ├── happy.jpg
│ ├── neutral.jpg
│ ├── sad.jpg
│ └── surprise.jpg
├── notebooks <- Jupyter notebooks.
│ └── deepface.ipynb
├── requirements.txt <- The requirements file for reproducing the analysis environment.
└── src <- Source code for use in this project.
├── __init__.py
└── app.py <- Integrates DeepFace and creates a Streamlit app for face detection, emotion recognition, and age, gender and race prediction.
- Clone the repository:
git clone https://github.com/carolinajimenez/faceapp
cd recsys
- Install the required packages:
pip install -r requirements.txt
- Run the Streamlit app:
streamlit run src/app.py
Project based on the cookiecutter data science project template. #cookiecutterdatascience
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