International Workshop on Biometrics and Forensics
28 th March, Valletta, Malta
Lúcia Carreira 1,2
Paulo Lobato Correia 1,2
Luís Ducla Soares 2,3
1 Instituto Superior Técnico
2 Instituto de Telecomunicações
3 ISCTE - Instituto Universitário de Lisboa
© 2005, it - instituto de telecomunicações. Todos os direitos reservados.

Introduction
Many applications and services depend on personal identification .
Identification methods are changing from being based on something people have to something people are – biometrics .
Application also in forensic scenarios.
Focus on palmprints:
30% of the prints found in crime scenes are of palms [1]
25% of the crime scenes only palmprints are found [2]
[1] S. K. Dewan, W. Elementary, “Scan a Palm, Find a Clue”, The New York Times, November 2003.
[2] http://www.businesswire.com, “El Paso police installs Sagem Morpho Palmprint System”, Business Wire, 2002.
2

Palmprint Recognition
Palmprint:
• Persistence
• Uniqueness
In case of small bruises or cuts the palm regenerates according to original pattern
Physiology of the palm:
• Major creases
• Minor flexion creases
• Ridge pattern
•
Minutiae
3

Palmprint Recognition
Using latent palmprints:
• Rotation and translation vs. registered palmprints
• Incompleteness → increased intra-user variability
•
Varying hand pressure, humidity, stretchability, skin condition, only partial print available
• Degradation (dust, smears, ...), often modelled as:
•
Gaussian noise
•
Salt and pepper noise
•
Motion blur
• Image acquisition:
•
Illumination and contrast changes
Proposal of motion blur detection and compensation technique
(inspired on technique developed for barcode reading).
4

Palmprint Database
THUPALMLAB database used in experiments:
• High resolution full palmprint database
• Image characteristics:
•
2040 x 2040 pixels
•
500 ppi resolution
•
80 subjects
•
2 hands
•
8 images/palm
[THUPALMLAB palmprint database – http://ivg.au.tsinghua.edu.cn/index.php?n=Data.Tsinghua500ppi]
5

Palmprint Database
A partial and degraded palmprint database was created modifying the original THUPALMLAB palmprints:
• Full palmprints were cropped into quarters
• Quarters were randomly rotated
6

Palmprint Database
Degraded Palmprints:
• Full palmprints were degraded with Gaussian noise, salt & pepper noise and motion blur
Gaussian noise ( μ = 0 and σ 2 = 0.1, 0.3 and 0.5)
7

Palmprint Database
Salt & pepper noise (p = 0.1, 0.2 and 0.5);
Motion blur ( l = 5, 10, 20 and 30).
8

Proposed System Architecture
9

Pre-processing
→ Gaussian Noise Removal
Local adaptive noise reduction filter (5x5):
10
Test palmprint Estimated palmprint

Pre-processing
→ Salt & Pepper Noise Removal
Median filter (3x3):
11
Test palmprint Estimated palmprint

Pre-processing
→ Motion Blur Compensation
An image blurred by uniform linear camera motion features periodic stripes in the frequency domain .
These stripes are perpendicular to the direction of motion.
Centered logarithmic power spectrum:
12

Pre-processing
→ Motion Blur Compensation
Blur Compensation
• Image Pyramid Construction
•
Down-sampling
•
Low pass filtering
•
Convolution with directional
9x9 filters.
13

Pre-processing
→ Motion Blur Compensation
Stripe detection:
.
Binarization of the images with a threshold of 10% the maximum intensity value
.
Image selection from the pyramid
.
Determine orientation
.
Convolution with a 11x11 directional filter rotated to the direction of the stripes.
14

Pre-processing
→ Motion Blur Compensation
15

Pre-processing
→ Motion Blur Compensation
Blur compensation by Wiener deconvolution:
16
Test palmprint Estimated palmprint

Pre-processing
→ Contrast Adjustment
Remapping of the pixel intensity distribution
17

Feature Extraction
Keypoint Extraction
Detection of SIFT keypoints location and descriptor
18

Feature Extraction
Hand Mask Creation
Image binarization (Otsu’s method)
Gaussian smoothing
Texture segmentation with a 9x9 entropy filter
19

Matching
The extracted keypoints are matched by comparing the
Euclidean distance between the keypoints descriptors.
A matching score is computed for each pair of palmprints.
20

Results
[1] A. Jain and M. Demirkus, “On Latent Palmprint Matching”, Dept. Computer Science, Michigan State Univ., East Lansing, Tech. Rep., May 2008.
[2] J. Dai and J. Zhou, “Multifeature-Based High-Resolution Palmprint Recognition”. IEEE Trans. Pattern Anal. Mach. Intell.
, vol. 33, no. 5, pp. 954-957, May 2011.
[3] S. Singh et al.
, “Improved Rotation-Invariant Degraded Partial Palmprint Recognition Technique”, in International Workshop on Biometrics and Forensics , Lisbon, 2013.
[4] M. Laadjel, F. Kurugollu, A. Bouridane and S. Boussakta, "Degraded Partial Palmprint Recognition for Forensic Investigations," in
International Conference on Image
Processing , 2009, pp. 1513-1516.
21

Conclusions
Comparison to other palmprint recognition systems is influenced by the databases considered.
THUPALMLAB contains ~20% images of poor quality due to degradation, blur and incompleteness.
Performance results for partial and degraded data are reasonable.
In many cases rank 10 results can be useful.
Good results even with relatively strong noise.
Proposed motion blur compensation technique works well for small blur lengths. Performance degrades as blur length increases.
22

23