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Hand Shape Recognition Using Very Deep Convolutional Neural Networks
Citation key rakowski-2018-icccv
Author Alexander Rakowski and Lukasz Wandzik
Title of Book Proceedings of the 2018 International Conference on Control and Computer Vision
Pages 8–12
Year 2018
ISBN 978-1-4503-6470-6
DOI 10.1145/3232651.3232657
Location Singapore, Singapore
Address New York, NY, USA
Publisher ACM
Series ICCCV '18
Abstract This work examines the application of modern deep convolutional neural network architectures for classification tasks in the sign language domain. Transfer learning is performed by pre-training the models on the ImageNet dataset. After fine-tuning on the ASL fingerspelling and the 1 Million Hands datasets the models outperform state-of-the-art approaches on both hand shape classification tasks. Introspection of the trained models using Saliency Maps is also performed to analyze how the networks make their decisions. Finally, their robustness is investigated by occluding selected image regions.
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