Advisor:Wen-Shiung Chen Student: Min-Chao Chang
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Transcript of Advisor:Wen-Shiung Chen Student: Min-Chao Chang
Discrete Finger and Palmar Feature Extraction
for Personal Authentication Junta Doi, Member, IEEE ,and Masaaki Yamanaka
Advisor:Wen-Shiung ChenStudent: Min-Chao Chang
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Outline
IntroductionImage acquisitionFeature Point DefinitionFeature Extraction & MatchingConclusion
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Introduction
Biometrics Physiological traits Behavioral traits
finger geometry observation Palmar flexion crease Hand anatomy
Hand geometry is considered to achieve medium security.
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Introduction
Advantages No time-consumptive image analysis Noncontact Real-time Reliable feature extraction Easily combinable with other traits
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Image Acquisition
Device: using a monochrome and/or color video camera Resolution: not require for the faster response /
major crease detection Propose: the palm is placed freely toward the video camera in front of a low-reflective plate
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Image Acquisition
Schematic photograph of the palm image acquisition device
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Image Acquisition
Finger alignment: use an image of the finger-close-together without bending
Enhance creases: 1. By CCD camera with polarizing filter 2. Lighting from a direction of 45 degrees wrist side 3. near infrared CCD camera
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Image Acquisition
Image quality: VGA of 640X480, 8bit gray levels the number of palm images is about 500, corrected
from about 50 subjects Noise reduction : use repetitive morphological
operations of erosions and dilations
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Feature Point Definition
Intersection points with circles
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Feature Point Definition
Illustration of tangential line at intersection points
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Feature Point Definition
The way of extract the skeletal line skeletonization thinning algorithm
The way of search the intersection points two dimensional matrix operator
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Feature Extraction
Finger Spreading and Skeletal Lines a. the middle finger skeletal axis remains unchanged b. when fingers are bring together, the skeletal lines deviate little
Feature extraction at the intersection points on skeletal lines
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Feature Extraction
Comparison of intersection points when fingers are spread apart And brought together
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Feature Extraction
Comparison of each finger skeletal line when fingers are spread apart (white lines) and wider apart (black lines).
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Feature Extraction
Orientations at the intersection points Examples of detected orientations at the intersection points
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Feature Extraction
missing points or additional points on the extended skeletal line in the palm region may occur in the new entry the middle finger matching is found to be the most reliable among the four
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Feature Matching Using Skeletal Lines
For the palm, it consists of the intersection points of the major palmar flexion creases or prominent creases, which are typically three palmar creases , on the extended skeletal line of each finger and also the orientations at the intersection points
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Feature Matching Using Skeletal Lines
The first feature vector( in middle finger) Distal Middle Proximal
The second, third and fourth feature vector Forefinger Ring finger Little finger
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Feature Matching Using A Mesh
a mesh is proposed and constructed by connecting laterally the corresponding intersection points on the adjacent skeletal lines
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Feature Matching Using A Mesh
Each lateral line to line distance depends on the width of the finger
The over all lateral line distances depend on the palm width
All the widths and lengths are personal and are combined with the oriented palmar intersection points
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Feature Matching Using A Mesh
Mesh Matching for Authentication the middle finger skeleton is selected to align
the meshes for the enrolled and the new Some deviates is caused by a palm image
variation due to the palm bending, though all the fingers are brought together
Compare of the enrolled and the new of the same palm
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Feature Matching Using A Mesh
Compare of mashes for different palms
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Feature Matching Using A Mesh
The mesh deviation between the two, is evaluated by calculating the root mean square deviation (rmsd) value.
δi is the positional difference at each mesh point N is the total number of the mesh points to be compared
The magnitude of the difference is measured in pixels and thereafter normalized by the parameters of the finger length and the palm width
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Feature Matching Using A Mesh
Rings and Mesh Points The ring wear has little effect on the feature
matching, if it is limited in size and number
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Feature Matching Using A Mesh
“finger-brought-together” image instead of the pegs
“stretched-or-straightened” image instead of the flat bottom plate
the bending is not so fatal, if it is urged to stretch or straighten out
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Results
Database : 50 users
Each user’s hand : 10 images were captured (total of 500 images ).
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Feature Matching Using A Mesh
Genuine and imposter rmsd distribution
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Conclusion
Our matching is multistaged: the first stage is matching for the authentication the second stage is based on four-finger procedure as a
usual matching the third stage is based on more detailed geometric
parameters such as the shape factors of each finger section or the palm
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Conclusion
This point-based matching brings about a robust and real-time processing of less than one second
The “brought-together fingers” and “stretched-and-straightened-out palm” are our instructions to the user
this noncontacting personal feature extraction method will easily in combination with the hand geometry, palm vascular pattern, and/or facial processing
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References
A. K. Jain and A. Ross, “A prototype hand geometry-based verification system,” in Proc. 2nd Int. Conf. Audio- and Video-based Biometric Personal Authentication (AVBPA), 1999, pp. 166–171
N. Duta, A. K. Jain, and K. Mardia, “Matching of palmprint,” Pattern Recognit. Lett., vol. 23, pp. 477–485, 2002
R. Sanchez-Reillo, C. Sanchez-Avila, and A. Gonzalez-Marcos, “Biometric identification threou hand geometry measurements,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1168–1171, Oct. 2000