Fingerprint Indexing Based on Minutia Cylinder-Code and Deep Convolutional Feature
SONG Dehua1, FENG Jufu1
1.Key Laboratory of Machine Perception(Ministry of Education), Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871
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.
宋德华, 封举富. 基于细节点柱形编码和深度卷积特征的指纹检索方法[J]. 模式识别与人工智能, 2018, 31(2): 175-181.
SONG Dehua, FENG Jufu. Fingerprint Indexing Based on Minutia Cylinder-Code and Deep Convolutional Feature. , 2018, 31(2): 175-181.
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2018, Vol. 31 
