faceprecision

Faceprecision is a comprehensive face analysis project leveraging advanced deep learning and computer vision techniques. This project includes a modules and source codes for detecting and analyzing various facial attributes such as age, emotion, gender, the presence of a mask, race and skin tone.

Features

• Face Detection: Utilize state-of-the-art YOLOv8 and non-face classification models for accurate face detection.
• Face Analysis: Leverage Multi-task Learning with Transfer Learning model for detecting age, emotion, gender, mask usage, and race/skintone.
• Face Recognition: Implement Face Recognition with pretrained FaceNet model using Transfer Learning to identify and verify individuals in images and videos.

Installation & Usage

Ensure you have Anaconda or Miniconda installed. Follow these steps to set up the environment:

• Create a Conda Environment:

  conda create -n faceprecision python=3.8
  conda activate faceprecision

• Clone the Repository:

  git clone https://github.com/MyNameIsPHP/faceprecision.git
  cd faceprecision

• Install Tensorflow GPU (you can follow the instruction on tensorflow.org) or example scripts below:

  ### For Windows 10/11
  conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
  python -m pip install "tensorflow==2.10"
  conda install cudnn
  # Verify install:
  python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
  
  ### For Ubuntu 20.04, 22.04
  conda install -c conda-forge cudatoolkit=11.8.0
  python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.12.*
  mkdir -p $CONDA_PREFIX/etc/conda/activate.d
  echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
  echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
  source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
  # Verify install:
  python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

• Install other requirements:

  pip install -r requirements.txt

• Then you can import the package and use its functionalities like demo.py:

  from faceprecision.FacePrecision import FacePrecision
  
  faceprecision = FacePrecision()

Predict output from image

result = faceprecision.predict("test.jpg", save_path="result.jpg")

Predict output from video

faceprecision.predict("test_video.mp4", save_path="processed_video.mp4")

Real-time Analysis: Perform face analysis in real-time using webcam

faceprecision.start_webcam()

How to train the Multitask Attention face analysis model

You can follow the steps in notebook.py or this Google Colab link below

Before training, download the pretrained DeepFace model by execute the command:

python download_stuff.py https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5 facenet512_weights.h5

After training, you can convert the .h5 model to .onnx and place the weights file in weights folder.

python convert_to_onnx.py multitask_attention_akatsuki.h5

If you want to choose which model to run, you can configure the FacePrecision instance:

from faceprecision.FacePrecision import FacePrecision

faceprecision = FacePrecision(
    detector_model='your_face_detection_model.pt',
    analyzer_model='your_face_analysis_model.onnx'
)

By default, the analyzer_model will be multitask_attention_akatsuki.onnx, you can download the weight in the section below

Weights

Multitask Attention Network face analysis weights

• multitask_attention_akatsuki.onnx
• multitask_attention_akatsuki.h5

YOLOv8 Face detection weights:

• yolov8_face_detection

Contributing

We welcome contributions! Please submit pull requests for improvements, bug fixes, or enhancements.

References

• ultralytics
• OpenCV