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Intelligent fingerprint quality analysis using online sequential extreme learning machine

机译：在线连续极限学习机的智能指纹质量分析

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摘要

Because the quality of fingerprints can be degraded by diverse factors, recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint authentication systems. This paper proposes an effective fingerprint quality analysis approach based on the online sequential extreme learning machine (OS-ELM). The proposed method is based not only on basic fingerprint properties, but also on the physical properties of the various sensors. Instead of splitting a fingerprint image into traditional small blocks, direction-based segmentation using the Gabor filter is used. From the segmented image, a feature set which consists of four selected independent local or global features: orientation certainty, local orientation quality, consistency, and ridge distance, is extracted. The selected feature set is robust against various factors responsible for quality degradation and can satisfy the requirements of different types of capture sensors. With the contribution of the OS-ELM classifier, the extracted feature set is used to determine whether or not a fingerprint image should be accepted as an input to the recognition system. Experimental results show that the proposed method performs better in terms of accuracy and time consumed than BPNN-based and SVM-based methods. An obvious improvement to the fingerprint recognition system is achieved by adding a quality analysis system. Other comparisons to traditional methods also show that the proposed method outperforms others.
机译：由于指纹的质量可能会因多种因素而下降，因此提前识别指纹的质量可能有助于提高指纹认证系统的性能。本文提出了一种基于在线顺序极限学习机（OS-ELM）的有效指纹质量分析方法。所提出的方法不仅基于基本的指纹特性，而且还基于各种传感器的物理特性。与其将指纹图像分为传统的小块，不如使用Gabor滤波器进行基于方向的分割。从分割的图像中，提取由四个选定的独立局部或全局特征组成的特征集：方向确定性，局部方向质量，一致性和脊距。所选功能集可抵抗导致质量下降的各种因素，并且可以满足不同类型的捕获传感器的要求。在OS-ELM分类器的帮助下，提取的功能集用于确定是否应接受指纹图像作为识别系统的输入。实验结果表明，与基于BPNN和基于SVM的方法相比，该方法在准确性和耗时方面均表现更好。通过添加质量分析系统，指纹识别系统有了明显的改进。与传统方法的其他比较也表明，提出的方法优于其他方法。

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