CPSC 601 Lecture Week 5 Hand Geometry. Outline: Hand Geometry as Biometrics Methods Used for Recognition Illustrations and Examples Some Useful Links References. Hand Geometry. Hand geometry is a biometric technique, which identifies person through the hand geometry measurements.
CPSC 601 Lecture Week 5
Characterized by its lengths, widths, shapes etc.
(1) Acquisition convenience and good verification performance
(2) Suitable for medium and low security applications
(3) Ease of Integration
(1)Large size of hand geometry devices
(2)Only used for verification
(3)Single hand use only
Picture taken from:
Hand Geometry Verification System
A flowchart for hand feature extraction and matching
(1) Change input RGB image into gray-level image
(2) Change the gray-level image into white-black image.
(3) Due to illumination problems, Median filtering to
remove noise is used. G(i,j) represents the gray value of pixel (i,j) after binarization, I(i,j) represent the original gray value.
(a) Input Image (b)Gray-Scale (c) Before filtering (d)After filtering
(1) Searching for the starting point
(2) Use the following algorithm
(3) All the coordinates of the border are recorded
(a) Binary Hand (b) Hand Contour
Purpose: To pinpoint the five finger tips and four finger roots.
Method: Depict the vertical coordinates of all contour pixels
By computing the first-order differential of vertical coordinates of f(i),
mark where differential sign changing from -1 to 1 as finger tips,
where differential sign changing from 1 to -1 as finger roots.
Generate a feature vector Vh, including 5 lengths of fingers, 10 widths
of fingers, and the width between v1 to v2.
Absolute distance metric
Weighted absolute metric
Euclidean distance metric
This distance doesn’t measure the difference between components of the feature vectors, but the number of components that differ in value.
This is a pattern recognition technique that uses an approach between the statistical methods and the neural networks. It is based on modeling the patterns with a determined number of Gaussian distributions, giving the probability of the sample belonging to that class or not. The probability density of a sample belonging to a class u is:
(a) Hand geometry sensing device (b) Incorrect placement of hand