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 Static Hand Gesture Recognition using Principal Component Analysis combined with Artificial Neural Network
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Tác giả hoặc Nhóm tác giả: Nguyen Trong Nguyen, Huynh Huu Hung, Jean Meunier
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Nơi đăng: Journal of Automation and Control Engineering (JOACE)
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; Số: 3(1);Từ->đến trang: 40-45;Năm: 2015
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Sign language is the primary language used by the deaf community in order to convey information through gestures instead of words. In addition, this language is also used for human-computer interaction. In this paper, we propose an approach which can recognize sign language, based on principal component analysis and artificial neural network. Our approach begins by detecting the hand, pre-processing, determining eigen-space to extract features and using artificial neural network for training and testing. This method has low computational cost and can be applied in real-time. The proposed approach has been tested with high accuracy and is promising.
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ABSTRACT
Sign language is the primary language used by the deaf community in order to convey information through gestures instead of words. In addition, this language is also used for human-computer interaction. In this paper, we propose an approach which can recognize sign language, based on principal component analysis and artificial neural network. Our approach begins by detecting the hand, pre-processing, determining eigen-space to extract features and using artificial neural network for training and testing. This method has low computational cost and can be applied in real-time. The proposed approach has been tested with high accuracy and is promising.
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