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A comparison between end-to-end approaches and feature extraction based approaches for Sign Language recognition
Oliveira, Marlon; Chatbri, Houssem; Little, Suzanne; O'Connor, Noel E.; Sutherland, Alistair
In this work we use a new image dataset for Irish Sign Language (ISL) and we compare different approaches for recognition. We perform experiments and report comparative accuracy and timing. We perform tests over blurred images and compare results with non-blurred images. For classification, we use end-to-end approach, such as Convolutional Neural Networks (CNN) and feature based extraction approaches, such as Principal Component Analysis (PCA) followed by different classifiers, i.e. multilayer perceptron (MLP). We obtain a recognition accuracy over 99% for both approaches. In addition, we report different ways to split the training and testing dataset, being one iterative and the other one random selected.
Keyword(s): Machine learning; Artificial intelligence; Image processing
Publication Date:
2018
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Publisher(s): IEEE Computer Society
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/22132/,
https://doi.org/10.1109/IVCNZ.2017.8402478
First Indexed: 2017-12-08 06:05:09 Last Updated: 2018-07-21 06:08:41