WAVELET TRANSFORMATION ATEB-GABOR FILTERS TO BIOMETRIC IMAGES

M. Nazarkevych, Yaroslav Voznyi, Sergiy Dmytryk
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引用次数: 2

Abstract

Biometric images were pre-processed and filtered in two ways, by wavelet- Gabor and wavelet Ateb-gabor filtration. Ateb-based Gabor filter is effective for filtration because it contains generalizations of trigonometric functions. The wavelet transform of Ateb-Gabor function was developed. The function dependence on seven parameters was shown, each of them significantly changes the filtering results of biometric images. The Ateb-Gabor wavelet research was performed. Graphic dependencies of the wavelet Gabor filter and the wavelet Ateb-Gabor filter were constructed. The appliance of wavelet transform makes it possible to reduce the complexity of calculating an Ateb-Gabor filter by simplifying function calculations and reducing filtering time. The complexities of algorithms for calculating the wavelet Gabor filter and the wavelet Ateb-Gabor filter have been evaluated. Ateb-Gabor filtration allows you to adjust the intensity of the entire image, and to change certain ranges, thereby changing certain areas of the image. Biometric images should have this property, on which the minucius should be contrasting and clear. Ateb functions have the property of changing two rational parameters, which will allow to make more flexible control of filtration. The properties of the Ateb function, as well as the possibility of changing the amplitude of the function, the oscillation frequency by the numerical values of the Ateb-Gabor filter, were investigated. By using the parameters of the Ateb function, you can get a much larger range of shapes and sizes, which expands the number of possible filtration options. You can also perform filtration once, taking into account the direction of the minucius and reliably determine the sharpness of the edges, rather than perform filtration many times. The reliability of results were tested using NIST Special Database 302 and good filtration results were shown. This is confirmed by the comparison experiment between the wavelet-Gabor filter and the wavelet Ateb-Gabor function based on the PSNR signal-to-noise ratio measurement.
生物特征图像的小波变换ateb-gabor滤波
采用小波- Gabor滤波和小波Ateb-gabor滤波两种方法对生物特征图像进行预处理和滤波。基于ateb的Gabor滤波器是有效的滤波,因为它包含了三角函数的一般化。提出了Ateb-Gabor函数的小波变换。给出了7个参数的函数依赖关系,每一个参数都会显著改变生物特征图像的滤波结果。进行了Ateb-Gabor小波研究。构造了小波Gabor滤波器和小波Ateb-Gabor滤波器的图形依赖关系。小波变换的应用简化了函数计算,减少了滤波时间,从而降低了Ateb-Gabor滤波器的计算复杂度。计算小波Gabor滤波器和小波Ateb-Gabor滤波器的算法的复杂性进行了评估。Ateb-Gabor过滤允许您调整整个图像的强度,并改变某些范围,从而改变图像的某些区域。生物识别图像应该具有这种特性,其上的负号应该对比鲜明且清晰。Ateb函数具有改变两个有理参数的特性,这将使过滤控制更加灵活。研究了Ateb函数的性质,以及用Ateb- gabor滤波器的数值改变函数的幅值和振荡频率的可能性。通过使用Ateb函数的参数,您可以获得更大范围的形状和尺寸,这扩展了可能的过滤选项的数量。您也可以执行过滤一次,考虑到负号的方向,并可靠地确定边缘的清晰度,而不是执行过滤多次。采用NIST专用数据库302对结果进行了可靠性测试,得到了良好的过滤效果。通过基于PSNR信噪比测量的小波- gabor滤波器与小波Ateb-Gabor函数的对比实验证实了这一点。
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