The DrivFace database contains images sequences of subjects while driving in real scenarios. It is composed of 606 samples of 640—480 pixels each, acquired over different days from 4 drivers (2 women and 2 men) with several facial features like glasses and beard.
The ground truth contains the annotation of the face bounding box and the facial key points (eyes, nose and mouth). A set of labels assigning each image into 3 possible gaze direction classes are given.
- The first class is the looking-right class and contains the head angles between -45º and -30º.
- The second one is the frontal class and contains the head angles between -15º and 15º.
- The last one is the looking-left class and contains the head angles between 30º and 45º.
The dataset can be downloaded [[here]] (https://archive.ics.uci.edu/ml/machine-learning-databases/00378/DrivFace.zip)
DrivImages.zip has the driver images. The image's name has the format:
- YearMonthDay_subject_Driv_imNum_HeadPose.jpg
i.e. 20130529_01_Driv_011_f .jpg is a frame of the first driver corresponding to the 11 sequence's image and the head pose is frontal. subject = [1:4], imNum = [001:...], HeadPose = lr (looking-right), f (frontal) and lf (looking-left).
drivPoints.txt contains the ground truth in table's format, where the columns have the follow information:
- fileName is the imagen's name into DrivImages.zip
- subject = [1:4]
- imgNum = int
- label = [1/2/3] (head pose class that corresponding to [lr/f/lf], respectively)
- ang = [-45, -30/ -15 0 15/ 30 15] (head pose angle)
- [xF yF wF hF] = face position
- [xRE yRE] = rigth eye position
- [xLE yL] = left eye position
- [xN yN] = Nose position
- [xRM yRM] = rigth corner of mouth
- [xLM yLM] = left corner of mouth
Katerine Diaz-Chito, Aura Hernández-Sabaté, Antonio M. López, A reduced feature set for driver head pose estimation, Applied Soft Computing, Volume 45, August 2016, Pages 98-107, ISSN 1568-4946,
The current version is based on Python 3.6. It is recommended to install [anaconda] (https://www.anaconda.com/download/#linux) and create a python 3.6 virtual environment using the below command
conda create --name drivface python=3.6
source activate drivfaceInstall the requirements using the below command:
cd DrivFace
pip install -r requirements.txtRun the below python file to download the dataset and preprocess the data. The script downloads the dataset, unzips the files and loads the images as h5py file
python Preprocess_Data.pyRun the below python file to create a model, train the model using transfer learning and test the model.
python train.py