人体手指静脉识别技术研究
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摘要
信息社会中,个人的身份认证和隐私保护已经变得越来越重要,基于生物特征的个人识别技术已经表现出了许多的优点,利用身体内部的生理学特征和身体的行为特征来识别个人身份是非常便利的。现在许多生物特征识别技术已经走进人们的生活,例如指纹、人脸等,广泛地应用于安检、门禁、加密等,但这些体表的生物特征很容易被伪造,难以满足信息安全和保密的需求。信息社会提出了基于体内生物特征识别的需求。随着近红外成像技术、模式识别和人工智能的发展,利用体内生物特征—手指静脉进行个人身份识别成为可能。本文通过对现有的手指静脉识别系统的深入分析,提出了基于近红外手指静脉图像的个人身份识别系统研制方案,为该技术的实用化提供了理论和实践基础。论文的主要工作及创新点有：
1)系统地研究了现有的人体手指静脉识别技术,分析了静脉识别的特点和实现的难点,提出了一种基于近红外手指静脉图像的个人身份识别系统方案。
2)研制了一套基于近红外手指静脉图像的个人身份识别原型机,该系统是由采集终端+PC联机组成。其中采集终端用来采集近红外手指静脉图像,由光源、滤波片、镜头、传感器和通信接口等模块组成,在采集终端中使用光强可调的近红外光透射手指,再用CMOS光学传感器采集手指静脉图像。由于每个手指厚度不同,本系统采集不同光强下的手指静脉图像序列进行图像融合,以提高手指静脉图像的质量。
3)提出一种图像融合的方法来扩展静脉图像的动态范围,该方法利用CMOS光学传感器的光照响应曲线进行成像质量评估,在不同光强下的手指静脉图像中进行最优图像块选择,再进行加权的融合,得到高动态范围的手指静脉融合图像,均匀化了图像的灰度和对比度,提高了手指静脉图像的质量。
4)提出一种基于灰度形态学的灰度剖面线谷底检测方法,使用组合形态学操作进行平滑滤波和波谷检测,提高了谷底检测的抗噪性能和灵敏度,应用到重复线形跟踪法提取静脉纹路是有效的,本文还提出一种图像缩放和中心增强滤波的方法来消除静脉跟踪轨迹上的毛刺噪声,可以提取出较理想的静脉纹路。
5)提出了一种基于三值图像的静脉特征匹配方法。针对重复线形跟踪法得到的跟踪轨迹中静脉边缘有随机性造成的模糊问题,本文先将跟踪轨迹划分为三值图像：跟踪次数较多的点分割为静脉主干,记为目标区域并赋值1,跟踪次数较少的点分割为静脉边缘,记为模糊区域并赋值0.5,其余点分割为非静脉,记为背景区并赋值0,使用此三值图像做为静脉特征模板,匹配时计算两模板间非0值区域间最小距离之和的平均值作为相似分数,分数越低则模板越相似。本文增加模糊区域的距离范数,减小模糊区域在匹配时的影响,弹性匹配能力强,不要求点与点之间的绝对位置匹配,对具有一定随机性的静脉边缘匹配具有较强的鲁棒性。
与现有静脉识别技术相比,本文深入分析了近红外手指静脉图像的采集和静脉特征提取和匹配。虽然本文的近红外手指静脉识别系统的原型机还无法完全满足现实个人身份识别需求,但其成果和经验也使手指静脉识别技术向实用化迈进了一大步。
The identity authentication and privacy protection are becoming more and more important in the information society. The personal identification technology based on biological characteristic has shown many advantages. It is convenient to using inherent physiology and behavior characteristics of human body. There are many types of biometric systems which are commercially available, such as fingerprints and faces used in safety inspection, entrance guard, encryption techniques, etc. However, these methods do not necessarily ensure confidentiality because the features are exposed on body surface which can be easily forged. Therefore, the patterns inside the human body are focused. The development of infrared imaging technique, pattern recognition and artificial intelligence makes personal identification based on human finger vein images possible. This paper proposes the overall developing paradigm of finger vein identification system via deep analysis of the hardware and software, and presents some effective techniques of finger vein image acquisition, vein feature extraction and matching. The main contents and innovations of the paper are as the follows:
First, a systematic description about the technical backgrounds of infrared finger vein identification was presented. The difficulties of finger vein identification were introduced. The overall strategy and the plan of building an infrared finger vein identification system were proposed, which consisted of four parts including infrared finger vein image acquisition, image preprocess, vein line feature extraction and matching.
Second, an infrared finger vein identification prototype device was designed, which composed of a PC and an on-line capture client. The capture client, composed of the light source module, filter/lens module, sensor module and communication interface module, was used to gather the infrared finger vein images. In the capture client, the dorsal side of the finger was illuminated by the infrared light, which could be auto-adjusted, and a CMOS camera with the infrared filter would capture the finger vein image. Because the fingers of humankind had different thickness, the system should capture a set of finger vein images under different light intensity in order to fuse the images into an ideal vein image.
Third, the image fusion method to get an enhanced dynamic range image was proposed, which based on the camera response function and multi-intensity infrared finger vein images. The algorithm aimed to divide images into blocks and select best informed image blocks with regard to camera response curve and then fuse them up with a global fusion function to remove block discontinuity. Through this algorithm, an image with uniform brightness and contrast was obtained.
Fourth, in vein feature extraction, the gray image morphology was used to detect the valley bottom of the gray profile vertical to the vein in the repeated line tracking. This method could smooth the profile and detect the valley bottom effectively. The result of the repeated line tracking was called the locus space as the finger vein feature extracted. In the post-process of the locus space, the method of image-resize and center-enhance was used to remove the flocky noise and to smooth the vein lines. The experiment result showed its availability.
Fifth, a tri-value template fuzzy match algorithm was presented to reduce the effect of fuzzy edges and tips of the vein feature image. The proposed method would segment the vein feature image into three areas:subject area, fuzzy area and background area, and then compute the average distance of non-background point to non-background area as the dissimilarity score between the two templates. The proposed approach did not require knowledge of correspondence among those points in the two templates.
Relative to the current state of finger vein recognition research, the theoretical analysis and techniques of vein image fusion, vein line extraction and vein feature matching in this thesis were creative.
1)系统地研究了现有的人体手指静脉识别技术,分析了静脉识别的特点和实现的难点,提出了一种基于近红外手指静脉图像的个人身份识别系统方案。
2)研制了一套基于近红外手指静脉图像的个人身份识别原型机,该系统是由采集终端+PC联机组成。其中采集终端用来采集近红外手指静脉图像,由光源、滤波片、镜头、传感器和通信接口等模块组成,在采集终端中使用光强可调的近红外光透射手指,再用CMOS光学传感器采集手指静脉图像。由于每个手指厚度不同,本系统采集不同光强下的手指静脉图像序列进行图像融合,以提高手指静脉图像的质量。
3)提出一种图像融合的方法来扩展静脉图像的动态范围,该方法利用CMOS光学传感器的光照响应曲线进行成像质量评估,在不同光强下的手指静脉图像中进行最优图像块选择,再进行加权的融合,得到高动态范围的手指静脉融合图像,均匀化了图像的灰度和对比度,提高了手指静脉图像的质量。
4)提出一种基于灰度形态学的灰度剖面线谷底检测方法,使用组合形态学操作进行平滑滤波和波谷检测,提高了谷底检测的抗噪性能和灵敏度,应用到重复线形跟踪法提取静脉纹路是有效的,本文还提出一种图像缩放和中心增强滤波的方法来消除静脉跟踪轨迹上的毛刺噪声,可以提取出较理想的静脉纹路。
5)提出了一种基于三值图像的静脉特征匹配方法。针对重复线形跟踪法得到的跟踪轨迹中静脉边缘有随机性造成的模糊问题,本文先将跟踪轨迹划分为三值图像：跟踪次数较多的点分割为静脉主干,记为目标区域并赋值1,跟踪次数较少的点分割为静脉边缘,记为模糊区域并赋值0.5,其余点分割为非静脉,记为背景区并赋值0,使用此三值图像做为静脉特征模板,匹配时计算两模板间非0值区域间最小距离之和的平均值作为相似分数,分数越低则模板越相似。本文增加模糊区域的距离范数,减小模糊区域在匹配时的影响,弹性匹配能力强,不要求点与点之间的绝对位置匹配,对具有一定随机性的静脉边缘匹配具有较强的鲁棒性。
与现有静脉识别技术相比,本文深入分析了近红外手指静脉图像的采集和静脉特征提取和匹配。虽然本文的近红外手指静脉识别系统的原型机还无法完全满足现实个人身份识别需求,但其成果和经验也使手指静脉识别技术向实用化迈进了一大步。
The identity authentication and privacy protection are becoming more and more important in the information society. The personal identification technology based on biological characteristic has shown many advantages. It is convenient to using inherent physiology and behavior characteristics of human body. There are many types of biometric systems which are commercially available, such as fingerprints and faces used in safety inspection, entrance guard, encryption techniques, etc. However, these methods do not necessarily ensure confidentiality because the features are exposed on body surface which can be easily forged. Therefore, the patterns inside the human body are focused. The development of infrared imaging technique, pattern recognition and artificial intelligence makes personal identification based on human finger vein images possible. This paper proposes the overall developing paradigm of finger vein identification system via deep analysis of the hardware and software, and presents some effective techniques of finger vein image acquisition, vein feature extraction and matching. The main contents and innovations of the paper are as the follows:
First, a systematic description about the technical backgrounds of infrared finger vein identification was presented. The difficulties of finger vein identification were introduced. The overall strategy and the plan of building an infrared finger vein identification system were proposed, which consisted of four parts including infrared finger vein image acquisition, image preprocess, vein line feature extraction and matching.
Second, an infrared finger vein identification prototype device was designed, which composed of a PC and an on-line capture client. The capture client, composed of the light source module, filter/lens module, sensor module and communication interface module, was used to gather the infrared finger vein images. In the capture client, the dorsal side of the finger was illuminated by the infrared light, which could be auto-adjusted, and a CMOS camera with the infrared filter would capture the finger vein image. Because the fingers of humankind had different thickness, the system should capture a set of finger vein images under different light intensity in order to fuse the images into an ideal vein image.
Third, the image fusion method to get an enhanced dynamic range image was proposed, which based on the camera response function and multi-intensity infrared finger vein images. The algorithm aimed to divide images into blocks and select best informed image blocks with regard to camera response curve and then fuse them up with a global fusion function to remove block discontinuity. Through this algorithm, an image with uniform brightness and contrast was obtained.
Fourth, in vein feature extraction, the gray image morphology was used to detect the valley bottom of the gray profile vertical to the vein in the repeated line tracking. This method could smooth the profile and detect the valley bottom effectively. The result of the repeated line tracking was called the locus space as the finger vein feature extracted. In the post-process of the locus space, the method of image-resize and center-enhance was used to remove the flocky noise and to smooth the vein lines. The experiment result showed its availability.
Fifth, a tri-value template fuzzy match algorithm was presented to reduce the effect of fuzzy edges and tips of the vein feature image. The proposed method would segment the vein feature image into three areas:subject area, fuzzy area and background area, and then compute the average distance of non-background point to non-background area as the dissimilarity score between the two templates. The proposed approach did not require knowledge of correspondence among those points in the two templates.
Relative to the current state of finger vein recognition research, the theoretical analysis and techniques of vein image fusion, vein line extraction and vein feature matching in this thesis were creative.
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