Solution of HW 2 and HW 3 of the Image based biometry course from University of Ljubljana (winter 2020).
See requirements.txt for required dependencies, note that the scripts were run with Python 3.7.6. The scripts can also be run in Python Interactive to see additional markdown styling.
Implements a simple U-Net to segment ears from headshot photos of people. See the segmentation.pdf report for details.
You need to place the AWE-W dataset in folder AWEForSegmentation it should contain four subfolders: train, trainannot, test and testannot. That is, the folder structure is exactly the same as the homowork zip.
Script awe_ear_segmentation.py can be run directly and must be in the same directory as AWEForSegmentation data folder. By default the training part is commented out and pretrained weights are loaded. Feel free to uncomment the code and retrain the model.
To get the pretrained weights download this folder and put it in the same directory as script.
Implements a classification model for ear recognition. See the recognition.pdf report for details. The already precropped ear images from the dataset are used. Alternatively, cropping based on the output of segmentation done in HW 2 could be easily implemented to obtain the full biometric pipeline based on ear modality.
You need to place the AWE-W cropped dataset in folder awe it should contain 100 subfolders, one for each subject. Image paths and train / test splits are extracted from awe-translation.csv file.
Script awe_ear_recognition.py can be run directly and must be in the same directory as awe data folder and the translation file. You can skip the training part and load the pretrained weights directly as indicated in code.