Dr. H B Kekre, Mr. V A Bharadi

Department of Computer Science, NMIMS University, MPSTME,

Ville Parle (West), Mumbai-56

hbkekre@yahoo.com, vinu_bharadi@rediffmail.com

Paper ID # 94

ICETET 2010

1. Introduction

2.Finger-knuckle Print

3.Region of Interest

4. Gradient & Orientation Calculation

5. Local & Angular Difference

7. Results

6. Coordinate System

8. Conclusion

Biometrics

Physical Characteristics – Fingerprints, Palmprint, Face, Iris etc.

Behavioral Characteristics – Signature, Speech, Gait.

Finger-Knuckle Print is an emerging biometric trait.

Figure 1. Finger-Knuckle Print

Acquisition Device

Figure2. Typical Finge-Knuckle-Print Image from Hong

Kong Polytechnic University FKP Database[7].

Acquisition

• Biometric Trait is acquired

• Converted to Standard Format

Pre-processing

• Noise Removal

• ROI Segmentation

Feature Extraction

• Extract Feature Vector

• Store Feature vectors for training

Matching

• Matching Feature Vector for classification

Consistent Area of the Biometric Trait.

Region of Interest is used for feature vector extraction.

Multistep process based on Gradient & Sum of Cosine of

orientation field.

Gives flexible selection of ROI.

Fast enough to implement in real time systems.

Based on the fact that the FKP images are rich in

texture. This texture information is used for orientation

firld calculation.

Orientation filed is used for Localizing the Phalangeal

joint which is the center of co-ordinate system of ROI.

Orientation Field & Coherence of FKP

Image (a) Field overlayed on FKP image

(b) Actual Plot of gradient orientation field

(c) Coherence of FKP Image (Block size is

16X16Pixels)

Testing Summary

Total Images

Tested

Successfu

lFailure

Average testing time

per Image

502 483 19 110 milliseconds

Proposed technique is tested on the Hong

Kong Polytechnic University Finger-

Knuckle-Print database [7], this database

comes with the ROI images, and we have

compared our results with the given ROI

images. The program is written in Microsoft

Visual C# 2005, tested on AMD Athlon

64FX, 1.8GHz Processor, Windows XP

SP3 Operating System (32 Bit).

In this paper we have proposed a new technique to

segment the region of interest of Finger-Knuckle-Print

images.

This technique can be used in the preprocessing step for

implementing FKP verification.

The technique is fast and takes average 110 ms to

segment the ROI.

We have used Gradient Orientation and field strength to

detect the center of ROI, the accuracy given by

proposed technique is 96.21%, this method is another

viable practical approach to real time FKP ROI

segmentation.

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Authors are very thankful to L. Zhang, Assistant Professor,

Biometric Research Centre (UGC/CRC), The Hong Kong

Polytechnic University, for providing the Finger-Knuckle-Print

database. This database has been a key resource for this research.