基于三高斯滤波的低质指纹图像增强方法

摘要

Low quality fingerprint recognition is an all-pervading problem of criminal investigation, which asks for extra manual efforts to address. Therefore, the fingerprint image processing method in accordance with human visual characteristics has certain practical value. This paper introduces the tri-Gaussian model of the concentric receptive field to the fingerprint image processing, and proposes a novel image enhancement algorithm especially for low-quality fingerprint image processing. The algorithm goes like this; firstly obtain the perceptual luminance of the neighbouring image by tri-Gaussian unilateral filtering; and then enhance the local contrast of the given fingerprint image. Based on analysis of the ridge-valley alternatedly distributing properties of fingerprint images and the disin-hibitory properties of concentric receptive field, we have obtained the adaptive parameter model of tri-Gaussian unilateral filter especially for fingerprint images as well as the local contrast adjustment parameters. The contrast experiments indicate that the proposed method is effective to enhance the global and local luminance, stress the details of dark areas, and especially appropriate to assist the low-quality fingerprint identification.%低质指纹图像在司法刑侦过程中普遍存在,往往需要人工参与鉴别.因此,符合人类视觉特性的指纹图像处理方法研究具有一定的实用价值.将非经典感受野三高斯数学模型引入指纹图像处理,提出一种新的低质指纹图像增强算法.首先通过三高斯单边滤波获得邻域图像的主观感觉亮度；然后对指纹图像进行局部对比度增强.通过分析研究指纹脊谷交替分布的特性,结合三高斯模型自身特性,得到针对指纹图像的三高斯单边滤波的参数自适应模型和局部对比度调整参数.对比实验结果表明,该方法取得了整体和局部的亮度增强效果,突出灰暗区域的细节特征,尤其适用于低质指纹图像的处理.