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A comparison between end-to-end approaches and feature extraction based approaches for Sign Language recognition


Marlon Oliveira, Houssem Chatbri, Suzanne Little, Noel O'Connor, Alistair Sutherland

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
Image and Vision Computing New Zealand (IVCNZ) 2017
Proceedings of the Image and Vision Computing New Zealand (IVCNZ) 2017
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
New Zealand
Research Group: 
Dublin City University (DCU)
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