Abstract
Liquid test methods included the authenticity of identification and pattern recognition. Because of the limitation, the authenticity of identification is less useful than pattern recognition. In drop fingerprint pattern recognition, the drop fingerprint data and the eigenvalues of fingerprint are both objects for study. The drop fingerprint feature extraction by waveform analysis can grasp the main features of the liquid, and reduce the amount of data to be processed, greatly improve the recognition efficiency. With the advantage in classification, the Probabilistic Neural Networks provide a good method to the drop fingerprint classification and recognition. By MATLAB simulation, a Probabilistic Neural Networks is established, 8 eigenvalues are set as the pattern stand for a liquid. In the experiment, the rate of correct recognition reach 97.5%.
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© 2011 Springer-Verlag Berlin Heidelberg
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Song, Q., Li, J. (2011). Drop Fingerprint Recognition Based on Feature Extraction and Probabilistic Neural Networks. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_49
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DOI: https://doi.org/10.1007/978-3-642-23896-3_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
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