Hand Gesture Recognition Project

This project aims to put your understanding of machine learning algorithms into practice with a real world problem.

Environment

you can download all the packages used using conda or pip

This will create new environment from scratch with all listed modules.

conda env create -f environment.yml

if this option stuck at solving environment step, you may need to run : conda config --set channel_priority strict
If you already have an env and want to install the modules only you can use The --prune option removes any packages that are not listed in the environment.yml file.

conda env update -f environment.yml
Or using pip

pip install -r requirements-pip.txt

Hand Gesture of the Colombian sign language

unzip the zipfile and put it all into data folder

It will handle everything from downloading into extracting data into /data folder.

However you would need to have gdown library installed. (included in requirements.txt)

We perform illumination processing in order to remove shadows and segment the hand and the background

We detect each hand's orientation and orient all images in the same direction

We perform edge detection

We use:

We found DAISY features to give the best results We then perform PCA to reduce the feature's dimensionality

We use a small Neural Network with 2 layers We use ReLU activation functions, categorical cross entropy loss function and adam optimizer

Accuracy: 79.6%