Combining singular points and orientation image information for fingerprint classification |
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| Authors: | Jun Li Wei-Yun Yau Han Wang |
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| Affiliation: | 1. School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;2. Institute for Infocomm Research, Singapore 119613, Singapore;1. College of Information Engineering, Yangzhou University, Yangzhou 225009, China;2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;1. Department of Mathematics, Southeast University, Nanjing, 210096, China;2. Department of Statistics, Nanjing Audit University, Nanjing, 211815, China |
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| Abstract: | Fingerprint classification is crucial to reduce the processing time in a large-scale database. Two popular features used are the singularities and orientation information and they are complementary. Therefore, an algorithm based on the interactive validation of singular points and the constrained nonlinear orientation model is proposed. The final features used for classification comprises the coefficients of the orientation model and the singularity information. This resulted in very compact feature vector which is used as input to an SVM classifier to perform the classification. The experiments conducted on the NIST database 4 show the effectiveness of the proposed method in producing good classification result. |
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