Irish sign language recognition using principal component analysis and convolutional neural networks
Oliveira, MarlonORCID: 0000-0003-0528-3807, Chatbri, Houssem, Little, SuzanneORCID: 0000-0003-3281-3471, Ferstl, Ylva, O'Connor, Noel E.ORCID: 0000-0002-4033-9135 and Sutherland, AlistairORCID: 0000-0003-0528-3807
(2017)
Irish sign language recognition using principal component analysis and convolutional neural networks.
In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), 29 Nov - 1 Dec 2017, Sydney, Australia.
Hand-shape recognition is an important problem in computer vision with significant societal impact. In this work, we introduce a new image dataset for Irish Sign Language (ISL) recognition and we compare between two recognition approaches. The dataset was collected by filming human subjects performing ISL hand-shapes and movements. Then, we extracted frames from the videos. This produced a total of 52,688 images for the 23 common hand-shapes from ISL. Afterwards, we filter the redundant images with an iterative image selection process that selects the images which keep the dataset diverse. For classification, we use Principal Component Analysis (PCA) with with K-Nearest Neighbours (k-NN) and Convolutional Neural Networks (CNN). We obtain a recognition accuracy of 0.95 for our PCA model and 0.99 for our CNN model. We show that image blurring improves PCA results to 0.98. In addition, we compare times for classification.
CAPES/Brazilian Science without Borders, process no.: 9064-13-3, SFI Research Centres Programme (Grant 13/RC/2106), IRC under Grant no. GOIPD/2016/61, SFI/12/RC/2289
ID Code:
22110
Deposited On:
30 Nov 2017 13:41 by
Houssem Chatbri
. Last Modified 07 Feb 2019 10:36