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http://dx.doi.org/10.6109/jkiice.2015.19.1.106

On-line Signature Recognition Using Statistical Feature Based Artificial Neural Network  

Park, Seung-Je (Department of Information and Telecommunication Engineering, Korea Aerospace University)
Hwang, Seung-Jun (Department of Information and Telecommunication Engineering, Korea Aerospace University)
Na, Jong-Pil (Department of Information and Telecommunication Engineering, Korea Aerospace University)
Baek, Joong-Hwan (School of Electronics and Information Engineering, Korea Aerospace University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.19, no.1, 2015 , pp. 106-112 More about this Journal
Abstract
In this paper, we propose an on-line signature recognition algorithm using fingertip point in the air from the depth image acquired by Kinect. We use ten statistical features for each X, Y, Z axis to react to changes in Shifting and Scaling of the signature trajectories in three-dimensional space. Artificial Neural Network is a machine learning algorithm used as a tool to solve the complex classification problem in pattern recognition. We implement the proposed algorithm to actual on-line signature recognition system. In experiment, we verify the proposed method is successful to classify 4 different on-line signatures.
Keywords
Kinect; Signature Recognition; Statistical Feature; Machine Learning; Artificial Neural Network;
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