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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; handshape recognition; machine learning; pattern recognition; sign language
Publication Date:
2018
Type: Conference item
Peer-Reviewed: Yes
Language(s): English
Institution: Dublin City University
Citation(s): Oliveira, Marlon and Chatbri, Houssem and Little, Suzanne and O'Connor, Noel E. and Sutherland, Alistair (2018) A comparison between end-to-end approaches and feature extraction based approaches for Sign Language recognition. In: Image and Vision Computing New Zealand 2017 (IVCNZ), 4-6 Dec 2017, Christchurch, New Zealand.
Publisher(s): IEEE Computer Society
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/22132/1/Houssem_-_IVCNZ_2017.pdf
First Indexed: 2017-12-08 06:05:09 Last Updated: 2018-07-12 06:05:49