利用掌紋及掌背血管特徵作生物認證

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林志隆(Chih-Lung Lin) 查詢紙本館藏

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資訊工程學系

論文名稱

利用掌紋及掌背血管特徵作生物認證
(Biometric Verification Using Palmprintsand Vein-patterns of Palm-dorsum)

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摘要(中)

最近，利用生物特徵作為身分認證技術的安全控制系統變得很重
要，過去幾年已有許多討論生物特徵認證技術的文章發表，這些文
章所描述的方法在使用上均受到某些限制，例如，擷取手掌影像時
需要一個固定手掌位置及方向的裝置，確保手掌在影像中之位置及
方向不變，以便可以從不同影像中相同之手掌區域萃取出生物特
徵；利用掌印影像當作認證的目標物；擷取影像時需要提供適當的、
充足的光線。這些限制造成使用者的不方便，也限制了生物特徵認
證技術的實用性。本論文發展出一個新方法，可以避免或消除上述
的限制。我們的方法可完全消除使用固定裝置(docking device)的
限制，也可克服使用掌印的限制，光線條件的限制也可避免。
論文中利用手掌的生物特徵提出兩個生物特徵認證技術，一個
是利用主掌紋為特徵，另一個技術是利用掌背血管作為認證的特
徵。這兩個方法的重要特性是不需事先知道認證目標物的相關知
識，所有參數均可自動設定；此外，上述的三種使用上的限制也可
避免。最後我們也提出實驗結果驗證方法之效果。

摘要(英)

Recently, personal verification based on biometric features
gradually becomes an important and highly demand technique for
security access systems. During the past, numerous literatures discussing
biometric verification using palm features have been reported. However,
they are all constrained by some limitations, such as the utilization of
docking devices to constrain the palm position while acquiring palmprint
images, the applying of inked palmprint images as the objects, and the
requirements of adequate lighting conditions, etc. These limitations
hinder the conveniences of users and the practicalities of verification
methods. In this dissertation, novel methods are devised and developed
to alleviate or remove theses limitations. In our work, the limitation
imposed by the docking devices is removed completely. Furthermore,
the inconveniences introduced by the applying of inked palmprint
images as objects are avoided. Finally, the restrictions of lighting
conditions are also avoided.
In this dissertation, we propose two approaches of biometric
verification based on the palm features, which are principal palmprints
and vein-patterns of palm-dorsa, respectively. The crucial characteristics
of the proposed methods are that no prior knowledge about the objects is
necessary, the parameters can be set automatically, and the limitations as
mentioned above can be alleviated.
In the palmprint verification approach, scanner is adopted as the
input device for capturing palmprint images with the advantages of no
palm inking and no requirement of docking device. Two finger-webs are
automatically selected as the datum points to define the Region of
Interest (ROI) in the palmprint images. Next, hierarchical decomposition
is employed to extract the principal palmprint features inside the ROI,
which includes direction and multiresolution decompositions. The
former extracts principal palmprint features from each ROI. The latter
processes the images with principal palmprint features to extract the
dominant points from the images at each resolution. Finally, normalized
correlation function is utilized to evaluate the similarity between two
palmprint images. Experiments were conducted on a wide variety of
palmprint images and the results are satisfactory with acceptable
accuracy rate (FRR: 0.75% and FAR: 0.56%). The results reveal that our
proposed approach is feasible and effective in palmprint verification
without the needs of docking devices or palm inking.
In the vein-patterns verification approach, an infrared (IR) camera
is adopted as the input device to capture the thermal images of
palm-dorsa. Likewise, two of the finger-webs are automatically selected
as the datum points to define the Region of Interest (ROI) on the thermal
images. Within each ROI, feature points of the vein-patterns (FPVPs) are
extracted by modifying the basic tool of watershed transformation based
on the properties of thermal images. According to the heat conduction
law (the Fourier law), multiple features can be extracted from each
FPVP for verification. Multiresolution representations of images with
FPVPs are obtained using multiple multiresolution filters (MRFs) that
extract the dominant points by filtering miscellaneous features for each
FPVP. A hierarchical integrating function is then applied to integrate
multiple features and multiresolution representations. The former is
integrated by an inter-to-intra personal variation ratio and the latter is
integrated by a stack filter. We also introduce a logical and reasonable
method to select a trained threshold for verification. The proposed
approach can achieve an acceptable accuracy rate (FRR: 2.3% and FAR:
2.3%). The experimental results demonstrate that our proposed approach
is valid and effective for vein-pattern verification.

關鍵字(中)

★ 掌背血管特徵
★ 掌紋特徵
★ 生物特徵認證

關鍵字(英)

★ Vein-patterns of Palm-dorsa
★ Palmprints
★ Biometric Verification

論文目次

Contents
Contents............................................................................................................. i
List of Figures ................................................................................................. iv
List of Tables................................................................................................. viii
Chapter 1 Introduction .................................................................................. 1
1.1 Motivation............................................................................................. 1
1.2 Survey of related works ........................................................................ 2
1.2.1 Verification Using Palmprint Images ......................................... 2
1.2.2 Verification Using Thermal Images of Palm-dorsa
Vein-patterns..................................................................................... 4
1.3 Overview of the Dissertation ................................................................ 5
1.4 Organization of the Dissertation ........................................................... 8
Chapter 2 Data Collection........................................................................... 10
2.1 Collection of Palmprint Images .......................................................... 10
2.2 Collection of Thermal Images of Vein-patterns ................................. 14
Chapter 3 Preprocessing and Region of Interest (ROI) Locating............ 20
3.1 Introduction......................................................................................... 20
3.2 ROI Locating of Palmprint images..................................................... 22
3.2.1 Palm Region Segmentation....................................................... 22
3.2.2 Finger-webs locating................................................................. 25
3.3 ROI Locating of Vein-pattern Images ................................................ 31
Chapter 4 Biometric Verification Using Palmprint Images........................ 35
4.1 Introduction......................................................................................... 35
4.2 Principal Palmprint Features Extraction Based on Hierarchical
Decomposition....................................................................................... 37
4.2.1 Directional Decomposition ....................................................... 38
4.2.2 Multiresolution Analysis with Multiresolution Filter............... 45
4.3 Template library construction ............................................................. 48
4.4 Verification ......................................................................................... 50
4.5 Experimental results............................................................................ 51
Chapter 5 Biometric Verification Using Thermal Images of Palm-dorsa
Vein-patterns .................................................................................................. 58
5.1 Introduction......................................................................................... 58
5.2 Feature Points and Multiple Features Extraction................................ 61
5.2.1 Feature Points Extraction .......................................................... 62
5.2.2 Multiple Features Extraction..................................................... 68
5.3 Multiresolution Analysis and Multiresolution Representation........... 68
5.4 Design of Integration Function ........................................................... 74
5.4.1 Integration Function for Multiple Features............................... 74
5.4.2 Integration Function for MRA.................................................. 77
5.5 Selecting the Trained Threshold ......................................................... 84
5.6 Experimental Results .......................................................................... 87
Chapter 6 Conclusions ................................................................................ 93
6.1 Conclusions......................................................................................... 93
6.2 Future researches................................................................................. 95
References ...................................................................................................... 97
Publication List ................................................................................................ 1
Journal papers ............................................................................................. 1
Conference papers....................................................................................... 1

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指導教授

范國清(Kuo-Chin Fan)

審核日期

2003-12-20

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