圆形Gabor小波算法用于指纹活性检测

O. Onifade, Paul Akinde, F. Isinkaye
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引用次数: 7

摘要

生物识别技术的使用日益增长,指纹识别系统是最有效和最流行的人格识别方法之一。传统的指纹传感器的功能是全内反射(TIR),这是一种捕捉手指外部特征的方法。因此,这使它容易受到欺骗攻击。活体检测是一种反欺骗方法,具有识别指纹生理特征的潜力。实验证明,用明胶、软糖和橡皮泥制成的假指纹可以很容易地欺骗传感器。因此,这种传感器的安全性得不到保证。本文利用圆形Gabor小波对采集到的指纹图像进行纹理分割,建立了一种安全、鲁棒的伪欺骗指纹识别算法。使用用于纹理分割的圆形Gabor小波算法对样本进行特征提取处理。使用FAR对结果进行评估,该方法衡量用户是否在虚假身份下被接受。FAR结果为0.03125,准确率为99.968%,显示了真实指纹和伪造指纹的显著差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Circular Gabor wavelet algorithm for fingerprint liveness detection
Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.
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