Hand orientation redundancy filter applied to hand-shapes dataset
Oliveira, MarlonORCID: 0000-0003-0528-3807, Chatbri, Houssem, Yarlapati Ganesh, Naresh, O'Connor, Noel E.ORCID: 0000-0002-4033-9135 and Sutherland, Alistair
(2018)
Hand orientation redundancy filter applied to hand-shapes dataset.
In: Applications of Intelligent Systems - AAPIS 2019, 7-12 Jan 2019, Las Palmas de Gran Canaria, Spain..
ISBN 978-1-4503-6085-2/19
We have created a dataset of frames extracted from videos of Irish
Sign Language (ISL) for sign language recognition. The dataset was
collected by recording human subjects executing ISL hand-shapes
and movements. Frames were extracted from the videos producing a
total of 52,688 images for the 23 static common hand-shapes. Given
that some of the frames were relativity similar we designed a new
method for removing redundant frames based on labelling the hand
images by using axis of least inertia - Hand Orientation Redundancy
Filter (HORF) - and we compare the results with an iterative method
- Iterative Redundancy Filter (IRF). This selection process method
selects the most different images in order to keep the dataset diverse.
The IRF dataset contains 50,000 images whereas the HORF consists
of 27,683 images. Finally, we tested two classifiers over the HORF
dataset and compared the results with the IRF dataset