Transcript Slide 1
Video Based Palmprint Recognition
Chhaya Methani and Anoop M. Namboodiri
Camera Based Palmprint Recognition
Unconstrained Palm Imaging
• Challenges
• Towards civilian biometric applications
Low resolution web cameras
Unrestricted & unconstrained imaging
• Palmprint as a biometric
Unique
Easy to capture
User Convenience
High mobility
Easy integration simplifies use in a multibiometric setup
Video Based Palmprint Recognition
Figure showing difference in two images taken from the same user
Background
Contrast
Noise
Illumination
Pose & scale
Matching samples from the same user under different lighting
Detect and discard completely washed out samples
User 2
User 1
• Previous Work
Hand extraction from background has been attempted [1]
Pixel noise is dealt by selection of good features viz. Gabor[2]
Noisy artefacts like blur, low image quality, shadow effects etc
Illumination variations addressed by Intensity Normalization[2]
Not effective for low contrast or fully saturated pixels
Scale variations[1]
Hand extraction from background
Pose variations[3]
Accuracies can be improved by imbibing illumination invariance
Aplplications of Camera Based Palmprint Imaging a) Access Control ,b) Multibiometric Image Capture c)Mobile unlocking
[1] JDoublet, et al. “Contactless hand Recognition Using Shape and Texture Features”, ICSP 2006
[2] Zhang et al. “Online Palmprint Recognition”, PAMI 2003
[3] Methani,Namboodiri. “Pose Invariant Palmprint Recognition Recognition”, ICB 2009
Video Capture & Pre-Processing
Registration
FTA
Base
Image +
2
12.75%
0
12.75%
0
Paramet
er Sets
Base
Image
Score
Fusion
T0
FTA
Base
Image +
6
FTA
Base
image +
10
FTA
24.30
0
19.25%
0
36.19%
0
13.99%
0
4.70%
0
5.79%
0
T1
7.64%
6.57%
8.07%
8.09%
5.35%
7.58%
4.5%
8.09%
T2
14.36%
9.44%
5.82%
13.15%
4.64%
10.9%
3.62%
11.8%
T3
3.69%
11.8%
3.40%
16.86%
1.9%
13.99%
7.75%
14.5%
• Change in pose causes difference in reflection on different parts of the
hand
Can be used to advantage by using more frames with different lighting
The user is asked to move the palm a little while recording the video to
capture lighting variations
• Completely washed out samples are rejected by measuring texture
content of the image, viz mean & variance of edge response
Frame Combination & Matching
• Frame combination is preferred over super resolution due to high computational
complexity of the latter and a lack of robust landmark points in the low textured palm
Registration usually done by finding corresponding landmark points in the two textures
The transformations then found is used to align the two views
images
In the absence of robust keypoints on palm, the entire edge map is used to iteratively come closer to •the
Average rule was experimentally found to perform better
edge map of the consecutive frame
Since the motion of hand is smooth, any two frames can be aligned by adding transformations
of consecutive frames
Frame 2
Technique used
Base Image
Base Image + 2
Base Image + 6
Base image + 10
Max. rule
18.24%
16.05%
19.34%
26.86%
Second Max. rule
18.24%
15.03%
15.48%
19.39%
Average value
18.24%
10.94%
9.55%
9.50%
Frame 3
• Matching: matching is done using Gabor filter responses.
Euclidean distance and gradient direction gives the nearest point in the consecutive frame
This is the approximate corresponding point
The same process is followed for each point on the edge map
Based on all the point matches, a transformation is computed and matches improved iteratively
Matching the entire pattern ensures a good matching
Observations and Conclusions
Results and Experiments
• First row shows results of score fusion as more frames are added
• Rows 2-5 compare results while increasing quality control (threshold of expected minimum
texture) for each of the combinations formed by adding 0, 2, 6 and 10 images respectively
Palm line variation with change in view
• Important to overlay images separated in timeline before combining
Frame 1
• Preprocessing
Detect unique frames based on camera capture rate using background substraction
Correct in-plane rotation & extract palm image Correcting for in-plane rotations and palm extraction
Compute edge map
Reject samples having very low texture;
identified by the quality of the edge map obtained
a)Two images from same user showing variation with illiumination
b)completely saturated sample
• Video, instead of a single image, can capture more variations on the
user’s palm
Only when image is parallel to imaging plane
• Video Capture
Video of duration approx 2 sec is recorded
Users are advised to hold the hand loosely and allow some motion of hand to
instigate illumination variation with view variation
The background and the camera is fixed, with no restrictions on pose lighting etc
Few frames from a recorded video
• Pose and illumination Invariance:
Two fold challenge
Linear axis and Semilog axis ROC curve showing improvement in accuracies using multiple frames
•Dataset: 100 users, 6 videos each
640x480 logitech web camera used
• Combining 11 frames in the feature domain (using
Gabor filter response as features) takes 1.4 secs on
MATLAB
•Parameters used for the Gabor filter experimentally
determined to be the follwing: Window size = 27, var =
6.4, freq = 0.08
• 3528 genuine comparisons and 1,75,065 imposter
comparisons are made
•Thresholds used on mean and variance of Gabor
response of images to reject “bad” samples before
running the algorithm
ROC curve on semilog
axis after rejecting
samples using threshold
T3 as
shown in the Table.
It can be seen that the
unusual drop in ROC is
lost, indicating that
certain washed out
samples in data caused
the problem
Center for Visual Information Technology
International Institute of Information Technology Hyderabad
http://cvit.iiit.ac.in
• Reason for average rule working better than max or second max seems to be the
inexact registration
• Slow rise in GAR(Genuine Accept Rate)initially in ROC curve indicates presence of some
imposter scores before genuine scores
Indicates washed out samples
• These samples are then rid of when thresholding is applied, saving the recognizer
computational efficiency otherwise spent on “bad samples”
Conclusions and Future Work
• Video Based Palmprint Recognition results in better accuracies than when single image is used
due to the presence of extra information in the additional frames
• EER reduced from 12.79% to 4.70% on frame combination and further to 1.9% on removing the
washed out samples
• As a follow up to this work, an improvement in registration process shall improve combination
strategies and hence result in better strategies