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Pose Invariant Palmprint
Recognition
Chhaya Methani and Anoop Namboodiri
Centre for Visual Information Technology
IIIT Hyderabad
IIIT, Hyderabad, INDIA
Palmprint Aquisition
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• Controlled pose, scale,
and illumination
• High accuracy
• Fixed Scanner/Camera
• Restricted Palm position
• Palmprint-Specific
• Can we use a generic
camera as the
acquisition device?
Unrestricted Palmprint Imaging
• Minimal Constraints
• Intuitive, user friendly
• New applications
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• Multibiometric sensor
Challenges
• Background
• Illumination
• Contrast
• Noise
• Pose
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• Scale
Previous Work
• Background
– Skin Color
– Hand Shape
• Illumination
– Normalize
• Noise
Stenger et al. “Model-Based Hand Tracking Using a Hierarchical
Bayesian Filter”, TPAMI 28(9), Sept. 2006
Shadow,Wrinkles,
Pixel Noise.
– Good features
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• Scale
JDoublet, et al. “Contactless hand Recognition Using Shape and
Texture Features”, ICSP 2006
Variations in Pose
• Induce perspective line distortions
• Associated with scale changes
• Performance degradation EER: ~22%
• Dataset: 100 palms, 5 images per palm.
• Solution Directions:
1. Compute Pose-Invariant Features
2. Correct Pose variations
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• Non-rigid transformations are difficult
to model
• Assumption of planarity
Invariance to Perspective Projection
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• Cross Ratio, defined by 5
coplanar points
• Assume a stretched out
palm to be planar
• Sensitive to point position
• Need reliable point
detection
• Point matches found
using SIFT
•
Zheng, Wang and Boult : “ Application of Projective Invariants in Hand Geometry Biometrics”,
IEEE Transactions on Information Forensics and Security, 2007.
Finding Pose Transformation
Parameters
• Palm considered a planar surface.
• Homography defines transformation parameters
between 2 planes given 4 point correspondences
are known.
–
– Where x'/c and y'/c is the resulting point.
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• 4 distinctive point correspondences needed.
Solution using Interest Points
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• We use a combination of stable
points and a set of interest
points as candidate matches.
• Stable/Valley points are the
consistent points.
• 4 valley points available.
• Only 2 can be used.
• Rest of the points must be
selected from the palm lines.
• Thus, we choose a bag of
candidate interest points.
• These points are refined later to
get reliable interest points.
Valley Points
Proposed Solution
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Image
Acquisition
Image
Preprocessing Image
&
Alignment
Palm Extraction
Feature
Extraction
Matching
Image Acquisition
• Fixed Camera and Background
• Flexible Palm pose
and position
• Natural Illumination
variations
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• Sample Image
Image
Acquisition
Image
Preprocessing
&
Palm Extraction
Image
Alignment
Feature
Extraction
Matching
Image Preprocessing & Palm Extraction
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• Finger valley points are used to extract ROI and correct
in-plane rotations
Image
Acquisition
Image
Preprocessing
&
Palm Extraction
Image
Alignment
Feature
Extraction
Matching
Proposed Solution – Image Alignment
• Assumption of planarity of the palm surface
• Homography can be used to estimate pose
• 4 distinct point correspondences needed.
• Use a combination of stable
points and interest points
Valley Points
• Other interest points?
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Back to the same problem!
Image
Acquisition
Image
Preprocessing
&
Palm Extraction
Image
Alignment
Feature
Extraction
Matching
Proposed Solution – Image Alignment
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• Similar
Assumingprocess
equal
of occurrence
for all palm
points
Descriptors
Correspondences
areprobability
made
is found
followed
using
using
for
11x11
correlation
the windows
second
on
the line,
a richly
pointinterest
set is chosen
on the
around
each
of thesampled
candidate
points
palm line
Image
Acquisition
Image
Preprocessing
&
Palm Extraction
Image
Alignment
Feature
Extraction
Matching
Proposed Solution – Image Alignment
• The
Input
Final
The
final
best
set
to of
RANSAC
transformed
set
inliers
of parameters
in both
based
image.
theHomography:
template
found by
andRANSAC
thethe
set 2image
are.
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used for
valley
points
the final
and transformation.
iterative selection of the other
two from the interest points.
Image
Acquisition
Image
Preprocessing
&
Palm Extraction
Image
Alignment
Feature
Extraction
Matching
Proposed Solution: Computing Features
and Matching
• Thresholded Gabor responses
dist(final) = min(dist(fixed), dist(corrected))
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•
D. Zhang, A. W. K. Kong, You, J., Wong M., “Online Palmprint Identification” , PAMI 2003.
Image
Acquisition
Image
Preprocessing
&
Palm Extraction
Image
Alignment
Feature
Extraction
Matching
Datasets
• 100 palms,
5 images per palm
• Pose variations up
to 45 degrees
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• 50 palms, from
PolyU dataset
• 10 synthetic poses
per palm:
0 - 45 degrees
Results
• Comparison of EER values
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Method
Synthetic Data
Real Data
0◦- 20◦
20◦-30◦
30◦-35◦
35◦-40◦
40◦-45◦
Fixed Pose
Approach
0.01%
3.24%
3.71%
16.93%
30.92%
22.07%
Blind Pose
Approach
16.48%
12.40%
11.14%
14.98%
11.92%
16.51%
Proposed
Approach
0.47%
4.19%
11.14%
14.98%
11.92%
8.71%
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Results: Synthetic Data
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Results: Real Data
Results
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•• (r)
(p)
(q)
:The
: There
GAR
drop
low
iscurve
aofeven
sharp
GAR
with
drop
inobserve
case
high
in the
ofFAR.
proposed
GAR.
approach is earlier.
Semilog
to
the highlighted
data.
• Indicates imposter
genuine pairs
pairswith
withlow
high
increased
similarity.
similarity.
similarity.
• Reasons: Inherent
Blur, wrinkles,
Pixel
saturation,
in the algorithm.
etc.
specular reflections of skin etc.
Video Based Palmprint Recognition
Base Image
Base Image + 2
Base Image + 6
Base image + 10
12.46%
10.92%
9.83%
7.87%
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• Successive addition of Gabor responses.
• Images shown after adding 2, 6 and 10 images respectively.
Conclusion/Observation
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• Proposed view invariant recognition system for Palmprint.
• Very difficult to find point correspondences for palm.
• Solution using point correspondence of stable and interest
points.
• RANSAC based Homography used to choose from approximate
point correspondences.
• Major role played by illumination variations and noise.
• Video based palmprint recognition is a possible solution.
• Future Work: To study the effects of video based palmprint
recognition in further in more detail.
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Thank You