Using the joint transform correlator as the feature extractor for the nearest neighbor classifier

Boon Yi Soon; Mohammad A. Karim; Mohammad S. Alam

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Using the joint transform correlator as the feature extractor for the nearest neighbor classifier

机译：Using the joint transform correlator as the feature extractor for the nearest neighbor classifier

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摘要

Financial transactions using credit cards have gained popularity but the growing number of counterfeits and frauds may defeat the purpose of the cards. The search for a superior method to curb the criminal acts has become urgent especially in thebrilliant information age. Currently, neural-network-based pattern recognition techniques are employed for security verification. However, it has been a time consuming experience, as some techniques require a long period of training time. Here, a fasterand more efficient method is proposed to perform security verification that verifies the fingerprint images using the joint transform correlator as a feature extractor for nearest neighbor classifier. The uniqueness comparison scheme is proposed toimprove the accuracy of the system verification. The performance of the system under noise corruption, variable contrast, and rotation of the input image is verified with a computer simulation.

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来源
《Optical Engineering》 |1999年第1期|39-46|共8页
作者
Boon Yi Soon; Mohammad A. Karim; Mohammad S. Alam;
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作者单位

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类光学仪器;
关键词
Nearest neighbor classifier; Joint transform correlator; Power spectrum subtraction technique; Uniqueness comparison scheme; Optical security verification;

Using the joint transform correlator as the feature extractor for the nearest neighbor classifier

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