biometrics

1. Why Hand Geometry
 What is the most effective biometric measurement?
 Each biometrics has its strengths and limitation
 It’s depend on application
 User acceptability is the most significant factor
 For many access control applications, like dormitory meal plan
access, very distinctive biometrics, e.g., fingerprint and iris, may not
be acceptable
 Hand geometry-based verification systems are not new and have
been available since the early 1970s

2. A Prototype Hand Geometry-based
Verification System
 A technique to measure of hand geometry is
introduced by using probability function
 Outines:
 Image Acquisition
 Feature Extraction
 Verification Phase

3. Image Acquisition
 The image acquisition system comprises of a light source, a camera, a
single mirror and a surface (with five pegs on it)
 Pegs serve as control points for appropriate placement of the right hand
of the user
 A 640 by 480 8-bit grayscale image of the hand is captured

4. Feature Extraction
 Typically features include
length and width of the fingers,
aspect ratio of the palm or
fingers, thickness of the hand,
etc.
 16 axes along which the
various features mentioned
above have been measured
 The hand is represented as a
vector of the measurements
selected above
 
16
2
1 ,...,
, f
f
f
F 

5. Feature Extraction
 is the gray values of the pixels along a axis
 A sequence of pixels along a measurement axis will not have an ideal
gray scale profile
 Background lighting, color of the skin, noise and etc.
 
Len
x
x
x
x
X ,...,
,
, 3
2
1


6. Feature Extraction
 Modeling profile:
 Assumption:
 The observed profile is obtained from the ideal profile by the addition of Gaussian
noise to each of the pixels
 The gray level of an arbitrary pixel along a particular axis is independent of the
gray level of other pixels in the line
 Operating under these assumptions, we can write the joint distribution of
all the pixel values along a particular axis as

7. Feature Extraction
 The goal now is to estimate using the observed pixel values
along the chosen axis
 Use the Maximum Likelihood Estimate (MLE) method to estimate
parameters q
 likelihood function:
e
s P
P &

8. Feature Extraction
 The parameters can now be
estimated iteratively
 The initial estimates of parameters
are obtained as follows:
 are estimated using the
gray values of the first pixels
along the axis
 are estimated using the
gray values of the pixels from
 are estimated using the
gray values of the pixels between
 The values of are
fixed for the system
2
B
and
B 
1
1 A
and
A 
2
2 A
and
A 
1
A
N
  Len
to
N
Len A2
,
   
B
B N
Len
and
N
Len 
 2
/
2
/
B
A
A N
and
N
N 2
1,

9. Feature Extraction
 Result:

10. Verification Phase
 Let represent the d-dimensional feature vector in
the database associated with the claimed identity and
be the feature vector of the hand whose identity has to be
verified.
 
d
f
f
f
F ,...,
, 2
1

 
d
y
y
y
Y ,...,
, 2
1


11. Exp. Result
 The hand geometry
authentication system was
trained and tested using a
database of 50 users
 Ten images of each
user's hand were captured
over two sessions
 In each session the
background lighting of the
acquisition device was
changed
 Thus a total of 500
images were made
available

12. Questions?

13. A Biometric Identification System Based on
Eigen-palm and Eigen-finger Features
 The identification process can be divided into the
following phases:
 Capturing the image
 Preprocessing, extracting and normalizing the palm and strip-like
finger subimages
 Extracting the eigenpalm and eigenfinger features based on the
PCA
 Matching and fusion
 A decision based on the (k, l)-NN classifier and thresholding

14. System Description

15. Preprocessing and Sub-images Extraction
(a) Example of a scanned hand image. (b) Binerized hand image.

16. Preprocessing and Sub-images Extraction
(a) The hand contour and the relevant points for finding the regions of interest.
(b) Processed finger on the hand contour.

17. Geometry and Lighting Normalization
(a) Original hand image with the regions of interest marked on it. (b) Palm
subimage. (c) Little-finger subimage. (d) Ring-finger subimage.
(e) Middle-finger subimage. (f) Index-finger subimage. (g) Thumb subimage.

18. Geometry and Lighting Normalization
 After the subimages have been extracted, a lighting
normalization using histogram fitting is applied.
 In this process, a target histogram G(l) is selected for
each of the six subimage
Subimages (a) before and (b) after the histogram fitting.

19. Lighting Normalization
Palm subimages with the corresponding histograms (a) before and (b) after the histogram fitting.

20. Extraction of the Eigenpalm and Eigenfinger
Features
Average palm and
finger images from
database.
Eigenpalms and eigenfingers obtained from the database (indicated by the
corresponding ordinal numbers): (a) the eigenpalms and (b) the
little-finger eigenfingers.

21. MATCHING AND FUSION AT THE
MATCHING-SCORE LEVEL
 The matching between corresponding feature vectors is
based on the Euclidean distance
 The distances are normalized and transformed into
similarity measures
 The frequency distribution of the distances of templates of the
same person is calculated.

22. MATCHING AND FUSION AT THE
MATCHING-SCORE LEVEL
 The normalized outputs of the six matching modules are
combined using fusion at the matching-score level.

23. Experimental Results (Recognition)
 110 People and 5 templates per person.
 Minimum Euclidean distance.

24. Experimental Results (Recognition)
Recognition results as a
function of the subspace
dimensionality for (a) palm
subspace and (b) finger
subspaces.

25. Experimental Results (Identification I )
 57 people (10 TPP) as clients and 70 people (10 TPP)
as imposter
 The first 7 templates as a train set for each client

26. Experimental Results (Identification II )
 127 people and 10 templates per person