Finger Print Knuckle Feature Extraction UsingMulti Scale Wavelet Edge Detection Method

Hamza Boucenna Electronics Department

National Polytechnic SchoolEl-Harrach, Algiers, Algeria

boucenna . h @ g m ail.c o m

Latifa Hamami Electronics Department National Polytechnic School

El-Harrach, Algiers, Algerial_ ha ma mi @ yahoo .fr

Abstract— Biometric identification using Finger Print Knuckle (FKP) is a reliable technology and has various advantages over other biometric traits. In this paper, the feature extraction of FKP is carried out using multi scale wavelet edge detection. The result and performance is compared with conventional edge detection techniques like Sobel and Canny methods. The experiment is carried out on a PolyU database and from the analysis it is found that the performance of multiscale edge detection using wavelet is much superior to that of Sobel and Canny for FKP feature extraction.Keywords— Finger print knuckle (FKP); Feature extraction; Edge detection; Multiscale wavelets; Sobel; Canny.

I. INTRODUCTION

Recognizing the identity of a person with high confidence is a critical issue in various applications, such as e-banking, access control, passenger clearance, etc. The need for reliable user authentication techniques has significantly increased in the wake of heightened concerns about security, and rapid advancement in networking, communication and mobility.

Edge detection is a very important area in the field of Computer Vision. Edges define the boundaries between regions in an image, which helps with segmentation and object recognition. They can show where shadows fall in an image or any other distinct change in the intensity of an image. Edge detection is a fundamental of low-level image processing and good edges are necessary for higher level processing [1].

The difficulties in designing an accurate and robust edge detection algorithm mainly come from two sources. First, there are tradeoffs in choosing an operator to pursue the best overall edge detection performance. Based on Yuille and Poggio’s result, the 2-D Laplacian-of-Gaussian (LOG) operator should be used because it is the only operator that has a constrained zero-crossing behavior in 2-D scale space which, in turn, lays a necessary foundation for scale space manipulations. However, an isotropic operator like the LOG is not optimal in terms of signal-to- noise ratio (SNR) and edge localization accuracy (ELA).The Canny edge detector has better SNR and ELA than the LOG. However, the local extrema of its output may have unconstrained behaviors in 2-D scale space . Moreover, the Canny. edge detector is obtained by simply extending its one-dimensional (1-D) version based on a linear constant cross-section edge model. As a result, Canny edge

detector is not optimal even in terms of SNR and ELA, except in the case when the detected edge is a straight line having a constant intensity. More recent efforts on finding an optimal edge detector can be found,. Unfortunately, the issue of establishing a more accurate edge model has still been overlooked [11][8].

A. Finger Knuckle AnatomyEach finger has three joints. There are three bones in each

finger called the proximal phalanx, the middle phalanx and the distal phalanx. The first joint is where the finger joins the hand called the proximal phalanx. The second joint is the proximal interphalangeal joint, or PIP joint. The last joint of the finger is called the distal interphalangeal joint, or DIP as shown in fig.1 [1][7][9].

Fig. 1. F in g e r an a t o my a n d p e r c e p e c t i v e o f a pp li c a ti o n e .

B. Finger KnuckleChoosing the biometrics is the challenging task for

researcher. Biometrics based authentication is just impossible to help us if we don't know what are the requirements. Biometrics authentication must provide the security level, unattended system, Spoofing and Reliability. Among all the modalities FKP broadly explored which has not yet attracted significant attention of researchers. Finger knuckle is user- centric, contactless and unrestricted access control. As it is contactless hence no chance of proof of physical presence i.e. antispoofing.

Finger knuckle has High textured region. Many samples are available per hand and independent to any behavioural aspect. No stigma of potential criminal investigation associated with this approach. Fig 2 shows the developed FKP image acquisition device. [7][2][3].

Fig. 2. (a) The outlook of the developed FKP image acquisition device; (b) a typical FKP image taken from the PolyU FKP database [7]; (c) the selection of ROI; (d) the cropped ROI image from the original FKP image shown in (c).

II. MULTISCALE EDGE DETECTION

A. Canny Edge DetectionCanny edge detection is an important step towards

mathematically solving edge detection problems. This edge detection method is optimal for step edges corrupted by white noise. Canny used three criteria to design his edge detector: reliable detection of edges with low probability of missing true edges, and a low probability of detecting false edges [8].

To be sure that the detected edges should be close to the true location of the edge.

There should be only one response to a single edge.These criteria, the canny edge detector first smoothesthe image to eliminate and noise.

It then finds the image gradient to highlight regions with high spatial derivatives.

B. Wavelet TransformThe continuous wavelet model we constructed here not

only explains the working mechanism of most classical edge detectors, but also has several significant advantages in practical applications. The scale of the wavelet used in our model can be adjusted to detect edges of different levels of scale. Also, the smoothing function used in the construction of a wavelet reduces the effect of noise. Thus, the smoothing step and edge detection step are combined together to achieve the optimal result.

The resolution of an image is directly related to the proper scale for edge detection. High resolution and small scale will result in noisy and discontinuous edges; low resolution and large scale will result in undetected edges. The scale is not adjustable with classical edge detectors, but with the wavelet model, we can construct our own edge detectors with proper scales, because image data is always discrete, the practical scale in images is usually integer. With the cascade algorithm

and the wavelet-based edge detection method, we can detect edges of a series of integer scales in an image. This can be useful when the image is noisy, or when edges of certain detail or texture are to be neglected [5][6].

The scale controls the significance of edges to be shown. Edges of higher significance are more likely to be kept by the wavelet transform across scales. Edges of lower significance are more likely to disappear when the scale increases.

III. FKP FEATURE EXTRACTION

A. PolyU knuckle databaseWe use the PolyU FKP database [4] to evaluate the

performances of the proposed method. The PolyU FKP database was collected from 165 volunteers, including 125 males and 40 females. Among them, 143 subjects are 20-30 years old and the others are 30-50 years old. The images were collected in two separate sessions. In each session, the subject was asked to provide 6 images for each of the left index finger, the left middle finger, the right index finger and the right middle finger. In total, the database contains 7920 images from660 different fingers. The original image size is 110x220. Figure.3 shows the images of fingers.

Fig. 3. Samples of the Polyu FKP database.

B. ROI EXTRACTIONIt is necessary to construct a local coordinate system for

each FKP image. With such a coordinate system, an ROI can be cropped from the original image for reliable feature extraction and matching [7]. The detailed steps for setting up such a coordinate system are as follows.St e p 1 : Determine the X-axis of the coordinate system.A Canny edge detector can easily extract the bottom boundary of the finger. Actually, this bottom boundary is nearly consistent to all FKP images because all the fingers are put flatly on the basal block in data acquisition. By fitting this boundary as a straight line, the X-axis of the local coordinate system is determined.St e p 2 : Crop a sub-image . The left and right boundaries of are two fixed values evaluated empirically.The top and bottom boundaries are estimated according to the boundary of real fingers and they can be obtained by a Canny edge detector.St e p 3 : Canny edge detection. Apply a Canny edge detectionto to obtain the edge map .

St e p 4 : convex direction coding for . We define an ideal model for FKP “curves”. In this model, an FKP “curve” is either convex leftward or convex rightward. We code the pixels on convex leftward curves as “1”, pixels on convex rightward curves as “-1”, and the other pixels not on any curves as “0”.

Fig. 4. Database illustrates this convex direction coding scheme.

Fig. 4 illustrates this convex direction coding sheme and the pseudo codes are presented as follows:Convex_Direction_Coding( )Input : Output : (convex direction code map)

;for each :if

;else if and

;else if ( and )or

( and );

else if ( and )or( and )

;end if

St e p 5 : determine the Y-axis of the coordinate system.For an FKP image, “curves” on the left part of phalangeal joint are mostly convex leftward and those on the right part are mostly convex rightward. Meanwhile, “curves” in a small area around the phalangeal joint do not have obvious convex directions. Based on this observation, at a horizontal position x (x represents the column) of an FKP image, we define the “convexity magnitude” as:

Fig. 5. Illustration for the ROI extraction process. (a) image which is obtained by a down-sampling operation after a Gaussian smoothing; (b) X- axis of the coordinate system, which is the line , fitted from the bottom boundary of the finger; (c) image extracted from ; (d) image obtained by applying a Canny edge detector on ; (e) image obtained by applying the convex direction codingscheme to ; (f) plot of for a typical FKP image; (g) line where ; and (h) ROI coordinate system, where the rectangle indicates the area of the ROI sub-image that will be extracted.

C. FKP FEATURE EXTRACTIONMallat defines a wavelet function of a zero average as ,

(1)

where W is a window being symmetrical about the axisX=x.W is of the size , where is the height of . The characteristic of the FKP image suggests that

will reach a minimum around the center of the phalangeal joint and this position can be used to set the Y-

Which is dilated with scale parameter s, and translated by u.

(2)

axis of the coordinate system. Let :

Then is set as the Y-axis.St e p 6 : crop the ROI image. Now that we have fixed the X- axis and Y-axis, the local coordinate system can then be determined and the ROI sub-image ROI, can be extracted with a fixed size. Fig. 4-a show example of the extracted ROI images.

Using wavelet transform more information on the edgescan be extracted. Edges are characterized mathematically byLipschitz regularity given by :

(3)

Mallat shows that if wavelet transform is Lipschitz α then the function Lipschitz α is :

(4) Multi Scale Wavelet Edge Detection Algorithm: The FKP image ROI is extracted and a wavelet transform of image is

EdgeDetector

Advantages Disadvantages Efficiency forFKP feature extraction

Sobel Detects Edges andOrientations etc ..

Scales not used,sensitive to noise etc ..

5%

Canny Finds error rates,better in noisy conditions etc ..

Complexcomputation,uses two thresholds etc ..

76%

Multi scalewavele

Preserves real edges,more efficient in noisy conditions etc ..

Edges in multidirection cannotbe detected ( only horizontaland vertical ) etc ..

92,6%

performed using Dyadic scaling levels. At each step the image is convolved with a wavelet to obtain the coefficients at that level it is then smoothed with Gaussian of increasing scale. The wavelet and Gaussian filtering is done. The modulus maxima image combines both the filtered images and is given as:

eliminate the noise and finds the image gradient to highlight the region with a high spatial derivative. The algorithm then tracks along these regions and suppresses any pixel that is not at maximum. The gradient array is further reduced by hysteresis.

The angular image is calculated using(5)

(6)

Lines of maxima are fond using modulus and angular image. A pixel is modulus maximum if it’s larger than its two neighbours along the angle of gradient vector a pixel considering having 8 neighbours is chosen a threshold is set for a maximum value and a pixel value is chosen. The edge point which is at maxima is gathered and multiscale image edges are formed. The sharpest of all edges are also detected from ROI extracted.[6]

Fig. 6. Multi scale edge detection.

Fig. 7. Wavelet transform of different types of edges throughout scales : (a) Originalsignal; (b) wavelet transform computed up to the scale ; (c)At each scale, each Diracindicates the position of a modulus location.

Canny detector is considered to be an optimum edge detector which has a low error rate and also finds the distance between the edge pixels. Canny first smooth’s the image to

Fig. 8. Feature extraction using Canny edge detector.

Sobel operator performs spatial gradient measurement of an image and finds the region of high spatial frequency that corresponds to edges. Sobel finds the approximate absolute gradient magnitude at each point in input gray scale image. The operator consists of 3x3 convolutions kernels [10].

Fig. 9. Feature extraction using Sobel edge detection

TABLE I. MASK FOR THE EDGE DETECTION

IV. CONCLUSION

Feature extraction is a key processing stage for an accurate biometric system. Conventional edge detection techniques are used in feature extraction. FKP feature extraction using edge detection techniques requires further research. Multi scale wavelet edge detection technique applied for FKP feature extraction gives a promising result compared to Canny and Soble. Future work could be implemented using the combination of the conventional methods to get an accurate FKP biometric system.

REFERENCES

[1] S.S. Kulkarni, R.D.Rout, “Secure Biometrics: Finger Knuckle Print,” International Journal of Advenced Research in Computer Engineering Vol. 1, Issue 10, Decembre 2012.

[2] L. Zhang, Y. Shen, “A Novel Riesz Transforms based Coding Scheme for Finger–Knuckle-Print Recognition”, H a n d - B as e d Bi o m e t ri cs ( I C H B ) , 2 01 1 I nt e r n a ti on al C o n f e r e n c e , H ong Kong

[3] A.Meraoumia, S.Chitroub, A. Bouridane “Multimodal Biometric Person Recognition System based on Fingerprint & Finger-Knuckle-Print Using Correlation Filter Classifier”, C o m mu ni c a t i o n s ( I CC ) , 2 0 1 2 I EEE I nt e r n a ti on al C o n f e r e n c e , O tt a w a, C a n a d a p ages 820 – 824.

[4] PolyU Finger-Knuckle-Print Database, ht t p : // w w w .c o m p . p ol y u . e du . h k / biometrics/FKP.htm

[5] S. G. Mallat, “A Theory for Multiresolution Signal Decomposition : The Wavelet Representation”, IEEE TRANS on pattern analysis and machine intelligence, vol. II, July 89.

[6] Y . Z h a i , X L i u , “ Adaptive Edge Detection Based on Multiscale Wavelet Features”, I nt e ll ig e n t C on t ro l a n d A ut o ma ti on , 2 00 6 . W C I C A 20 0 6 . T h e S i x t h W orl d C o n gr e ss o n ( Volume:2 )

[7] L. Zhang, D. Zhang, D.Zhang, “Finger-knuckle-print: A new biometricidentifier”, I ma g e Pro c e ssi n g ( I C I P ) , 2 0 0 9 I ma g e Pro c e ssi n g ( I C I P ) , 2 00 9 1 6 t h I EEE I nt e r n a ti on al C o n f e r e n c e , C airo, Egypt.

[8] J. Canny, MEMBER, IEEE, “A Computational Approach to Edge Detection”, IEEE TRANS on pattern analysis and machine intelligence, VOL. PAMI-8, NOVEMBER 1986.

[9] K . Y . Ch e n g , A . K u mar, “Contactless Finger Knuckle Identificationusing Smartphones”, BIOSIG, page 1-6. IEEE, (2012)

[10] W. Gao, X. Zhang, L. Yang, H. Liu, “An improved Sobel edge detection”, C o m p ut e r S c i e n ce a n d I n for ma ti o n Te c hn olo g y ( I CC S I T ) , 2 01 0 3 r d I EEE I nt e r n a t i o n al C o n f e r e n ce .

[11] R.J. Qian, T. S. Huang, “Optimal edge detection in two-dimensionalimages”, I ma g e P r o c e ssi n g , I EEE T r a n s, V ol, 5, July 1996