基于细节点柱形编码和深度卷积特征的指纹检索方法

doi:10.16451/j.cnki.issn1003-6059.201802009

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Abstract In the typical fingerprint indexing method based on minutia cylinder-code(MCC) feature, the minutiae local structure is adequately taken into account. Since the global structure of fingerprint is ignored, the accuracy of fingerprint retrieval is limited. Therefore, deep convolutional neural network is employed to learn the global feature(deep convolutional feature) of fingerprint. Then, the MCC and deep convolutional feature are fused to improve the fingerprint indexing accuracy. Experiments are carried out to compare the proposed method with other prominent approaches on three benchmark databases. Besides, the property of deep convolutional feature is analyzed. Experimental results show that the proposed method effectively improves the accuracy of fingerprint indexing.

Key words： Fingerprint Indexing Deep Convolutional Neural Network Minutia Cylinder-Code Feature Representation

Received: 12 May 2017

ZTFLH:

TP 391

Fund:Supported by State Key Program of National Natural Science Foundation of China(No.61333015)

About author:: SONG Dehua, Ph.D. candidate. His research interests include machine learning, pattern recognition and computer vision.FENG Jufu(Corresponding author), Ph.D., professor. His research interests include image processing, pattern recognition, machine learning and biometric recognition.

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SONG Dehua,FENG Jufu. Fingerprint Indexing Based on Minutia Cylinder-Code and Deep Convolutional Feature[J]. , 2018, 31(2): 175-181.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201802009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I2/175

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