Human Authentication Process Using Finger Knuckle Surface with Artificial Neural Networks Based on a

Hybrid Feature Selection Method

Mobarakol Islam, Md. Mehedi Hasan and M. M. Farhad Dept. of Electronics & Communication Engineering,

Khulna University of Engineering & Technology, Khulna-9203, Bangladesh

{mobarak.islam, mehedi_ece & ronieee08 }yahoo.com

Tanzina Rahman Tanni Dept. of Electronics & Communication Engineering,

BRAC University, Dhaka-1212, Bangladesh tanni.tanzina@yahoo.com

Abstract An improved human authentication process using knuckle surface for personal identification has shown promising results. The texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. In this paper we proposed a new approach for efficient and more secure personal identification using knuckle surface. A specific data acquisition device is constructed to capture the finger knuckle surface images, and then an efficient finger knuckle print algorithm is presented with trained neural network. The finger back surface images from each of the users are normalized to minimize the scale, translation and rotational variations in the knuckle images. The main attraction of this proposed method is that a hybrid feature selection method of Lempel-Ziv Feature Selection and Principle Component Analysis is used for feature extraction and an artificial Neural Network based on Scaled Conjugate Gradient is used for the recognition. The experimental results from the proposed approach are promising and confirm. Compared with the other existing finger-back surface based biometric systems, the proposed system is more efficient and can achieve higher recognition rate in real time.

Keywords: Human detection; finger geometry; finger knuckle surface; hybrid feature selection; artificial neural network.

I. INTRODUCTION In bioscience human detection is the measurement of body parts or actions to identify a person and can be used for both identification one to many or verification one to one. Body parts can include a fingerprint, finger veins, an iris, the retina, DNA, hand or face measurementsan actions or behavioral biometrics can include person’s gait, voice, signature or keystrokes [1].A new observation of Finger-print recognition that uses state of the single steak in the finger-print image [2]. An iris patterns are unique across people. Only the iris bit code template specific to an individual need be stored for future identity verification [3]. The retinarecognition is recognized as user acceptance of what is at times considered an invasive technique, this limited acceptance was caused in part by the relatively high cost of signal acquisition [4].The human identification by gait can be achieved without any

knowledge of internal or external camera parameters [5]. A signature to the online detection of program memory and control flow error caused by transient and intermittent faults [6]. The finger surface posses unique patterns that have been utilized in the personal identification [7], [8]. The above works made a good effort to validate the uniqueness of biometric features in the outer finger surface; however, they did not provide a practical solution to establish an efficient system using the outer finger surface. We proposed a new system of human authentication process using knuckle surface.

Knuckle surface print is highly unique and makes this surface a distinctive biometric identifier. Knuckle surface refers to the inherent skin pattern of the outer surface around the phalange joint of one’s finger. The features of finger geometry can be collected from the same image, at the same time and integrated to improve the performance of the system. The peg-free imaging of the knuckle surface is highly convenient to users.

In this proposed system an improved human authentication process using knuckle surface has been proposed, first system is composed of a finger knuckle surface image acquisition device and a data processing module. The strong noise points are removed by using an adaptive median filter which smoothes the image selectively according to the decimal object scale, then, the remaining weak noise points are removed by using an adaptive Gaussian filter [9]. Gaussian noise is characterized by adding to each image pixel a value from a zero-mean Gaussian distribution. The zero-meanproperty of the distribution allows such noise to be removed by locally averaging pixel values [10]. We use canny edge detector for smoothing filter given the criteria of detection, localization and minimizing multiple responses to a single edge. Then a hybrid feature selection of the Lempel-Ziv Feature Selection (LZFS) and Principle Component Analysis (PCA) is used for feature extraction. Feature selection is an essential task for high dimensional data set. Because selecting the most representative features as inputs to the machine

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condition monitoring system presents another challenge. The principal component analysis (PCA) technique, also known as the Karhunen–Loeve transform, has been investigated before by researchers for signal and image processing [11], [12]. Principal component analysis is a popular tool for data analysis and dimensionality reduction. Complexity tends to be used to characterize something with many parts in intricate arrangement. Complexity of any system is measured to solve real world problems [13]. Lempel and Ziv proposed a useful complexity measure, which can characterize the degree of order or disorder and development of spatiotemporal patterns [14]. The Lempel-Ziv algorithm objectively and quantitatively estimates the system complexity through the change process of system structure, and has overcome the limitation of depicting the complexity through characteristic quantities of statistical complexity [15]. Lempel-Ziv Complexity (LZC) is a good mathematical tool to compute the number of unique patterns in a time series [16]. Finally an Artificial Neural Networks based on Scaled Conjugate Gradient method is used for Recognition. The proposed mechanism is compared with other’s related works [17] where it gives promising results.

II. PROPOSED MECHANISM The whole mechanism of this human authentication process is briefly described in below:

.

Fig. 1 Flow chart of the Proposed Human Identification system

The main motivation for the personal authentication by using knuckle surface print is the immense variation of the knuckle pattern to the human to human. The variation of the knuckle image pixel is observed by the hamming distance (HD) method where XOR technique is applied to determine the

pixel-wise variation among the human knuckle surface print. Gaussian noise filter is used to remove the noise from the database image. Another important part of this proposed system is hybrid feature selection method for the NeuralNetwork training and testing thewhole database. The consecutive steps of this human authentication process are given below. Step1: Collect database of human finger knuckle surface

imagesby the image acquisition device. Step2: Select the region of interest (ROI) by resizing the

images of collected database. Step3: Gaussian Noise filter is used to remove the noise from

the images. Step4: Canny edge detection method is used to detect the edge

of the final image. Step5: A hybrid feature selection method of PCA (Principle

Component Analysis) & LZFS (Lempel-Ziv Feature Selection) to extract the main feature database and compress it as our desired size.

Step6: The main recognition with database and testing image is executed by an Artificial Neural Network (ANN). Here we used an improved learning method called SCG (Scaled conjugate gradient) to train the neural network with the selected database.

III. EXPERIMENTAL STUDIES

A. STATISTICAL INDEPENDENCE

The main motivation of selecting Knuckle print for the Human Authentication is the big difference of the knuckle pattern among the human. The test of statistical independence is implemented by the simple Boolean Exclusive-OR operator (XOR) applied to the 2,048 bit phase vectors that encode any two knuckle patterns, masked (AND'ed) by both of their corresponding mask bit vectors to prevent non-knuckled artifacts from influencing knuckle comparisons. The XOR operator detects disagreement between any corresponding pair of bits, while the AND operator ensures that the compared bits are both deemed to have been uncorrupted by the noise. The norms of the resultant bit vector and of the AND'ed mask vectors are then measured in order to compute a fractional Hamming Distance (HD) as the measure of the dissimilarity between any two knuckle patterns, whose two phase code bit vectors are denoted {codeX, codeY} and whose mask bit vectors are denoted {maskX, maskY}:

HD codeX codeY maskX maskYmaskX maskY

TABLE I STATISTICAL INDEPENDENCE OF THE KNUCKLE PRINT BETWEEN DIFFERENT PERSON IN DIFFERENT FINGERS

Finger HD Left index 0.42

Right index 0.37 Left middle 0.29

Right middle 0.35

Edge detection (Canny Edge)

Feature assortment (PCA+LZC)

Artificial Neural Network training

Noise filter (Gaussian Noise Filter)

Select the ROI (Compression)

Database image (Knuckle surface)

Recognition

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B. ROI Extraction

From the database image we need to extract the reliable region of interest (ROI). For this purpose we should define a coordinate system on the knuckle finger print image. This coordinate system will be helpful for cropping an ROI from the original database. At first we have to determine the bottom boundary of the knuckle finger print by canny edge detector. The X axis of the coordinate system is determined by assuming this boundary as a straight line. Now in order to have a sub image we have to crop the original image. The top and bottom boundaries of this sub image are selected according to the boundary of real finger which can be determined by a canny edge detector. And the left and right boundaries of the sub image are selected empirically. Then canny edge detector is used to obtain the edge map of the subimage.This edge map forms some curves and the curves are convex leftward on the left part of phalangeal joint and those on the right part of phalangeal joint are mostly convex rightward. The pixels of the leftward curves are treated as “1” and the pixels of the rightward curves are treated as “-1” which is shown in Figure 2. Finally, based on the observation the Y axis of the coordinate system is determined at a point x around the phalangeal joint that do not have any obvious convex directions.

Fig. 2 Illustration for convex direction coding

Now the coordinate system is simple to determine as we have already fixed the X axis and Y axis. And from this the ROI sub image can be extracted. Figure 3(b) shows an example of the extracted ROI image.

(a) (b)

Fig. 3 (a) finger knuckle print image (b) ROI of (a)

C. Neural Network Training

The well known artificial neural networks training algorithm is Backpropagation (BP). But for the slow convergence and poor generalization ability of BP we used an improve algorithm of the called Scaled Conjugate Gradient (SCG) for neural networks training. The training database of the knuckle image is taken from the left and right index and middle finger’s knuckle print. A hybrid feature selection method combined of the PCA (Principle Component Analysis) and LZFS (Limpel-Ziv Feature Selection) for extracting feature image and compressed. At first PCA is used for principle component selection from the database print. After PCA feature vector it is converted to the binary form and Applied Limpel-Ziv Complexity (LZC) to determined the complexity for the different person database print. The higher complexity means the higher variation between the pixel-wise patterns of the knuckle image. Finally select the higher complexity pixel value for the neural network training.

(a)

(b)

(c)

Fig. 4 (a) segmented finger knuckle image (b) Gray image. (c) Binary image

The training and testing of the neural networks for the different person finger knuckle is observed in the Table 2 and

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Fig. 5 shows the training error curve for left index finger. Here it is shown that the detection accuracy for the left index finger knuckle surface with SCG algorithm is the higher than other’s observation. The highest accuracy of the recognition is 96.03% for the left index knuckle with SCG neural networks training algorithm. It is because the highest Hamming distance of this finger’s knuckle surface.

TABLE II PERSONAL RECOGNITION RESULT FOR 100 HUMAN KNUCKLE SURFACE WITH 5 DIFFERENT ANGLE OF THE KNUCKLE PRINT

Fig. 5 Training errors vs. no. of iteration of BP and SCG for left index finger

D. Comparison with Related Works

The proposed mechanism of this paper gives promising result as experimental observation. It‘s also compared with a previous works called EigenKnuckles and Fisherknuckles. In the Table 3, the proposed method recognition error rate is 3.97% where it 13.92% and 12.66% for the previous works EigenKnuckles and Fisherknuckles respectively. So from this comparison it cleared that proposed method outperform with others related works. TABLE III COMPARISON OF THE PROPOSED METHOD WITH A RELATED WORKS

INDEPENDENCE OF THE KNUCKLE PRINT

Mechanism Error rate Accuracy Proposed 3.97% 96.03%

EigenKnuckles 13.92% 86.08% Fisherknuckles 12.66% 87.34%

IV. CONCLUSION In this proposed system we presented a new approach for human authentication using finger knuckle surface images. The system is rigorously experimented on specially acquired

finger-back image database from 105 users and achieved promising result demonstrated the efficiency and effectiveness of the proposed system. Compared with other existing human finger knuckle surface based systems, the proposed finger knuckle surface authentication has merits of high accuracy, high speed, small size and more secure. This is especially useful and effective in real commercial applications and a great potential to be future improved.

REFERENCES [1] G.K.O Michael, T. Connie and A.T.B Jin, “Robust palm print and

knuckle print recognition system using a contactless approach”, In the proceeding of 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 323 – 329, 2010.

[2] B. Saropourian, “A new approach of finger-print recognition based on neural network” , In the proceeding of International Conference on Computer Science and Information Technology (ICCSIT), pp. 158 - 161, Aug 2009.

[3] S. Venugopalan and M. Savvides, “How to Generate Spoofed Irises From an Iris Code Template”, IEEE Transactions on Information Forensics and Security, pp. 385 – 395, June 2011.

[4] H. Borgen, P. Bours and S.D. Wolthusen, “Visible-Spectrum Biometric Retina Recognition”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing(IIHMSP ), pp. 1056 – 1062, Aug. 2008.

[5] M. Goffredo, I. Bouchrika, J.N. Carter and M.S. Nixon, “Self-Calibrating View-Invariant Gait Biometrics”, Systems, Man, and Cybernetics (Part B), pp. 1083-4419, Aug. 2010.

[6] Yung-Yuan Chen, “Concurrent detection of control flow errors by hybrid signature monitoring”, IEEE Transactions on Computers pp. 1298 – 1313, Oct. 2005.

[7] D. L. Woodard, P. J. Flynn, “Finger surface as a biometric identifier”, Computer Vision and Image Understanding, vol. 100, pp. 357-384, Aug. 2005.

[8] S. Malassiotis, N. Aifanti, and M. G. Strintzis, “Personal Authentication using 3-D finger geometry”, IEEE Trans. Information Forensics and Security, vol.1, no.1, pp.12-21, Mar. 2006.

[9] Xiaoliang Qian, Lei Guo, Bo Yu, “An Adaptive Image Filter Based on Decimal Object Scale for Noise Reduction and Edge Detection”, International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 461 – 465, July 2010

[10] A.K.Jain, Fundamentals of digital image processing, Prentice Hall, Englewood cliffs, 1989.

[11] V. Algazi, K. Brown, and M. Ready, “Transform representation of the spectra of acoustic speech segments with appliances, part I: General approach and application to speech recognition”,IEEE Trans. SpeechAudio Process., vol. 1, pp. 180–195, 1993.

[12] L. Sirovich and L. Keefe, “Low dimensional procedure for characterization of human faces”,J. Opt. Soc. Amer., vol. 4, pp. 519–524, 1987.

[13] T. D. Jorgensen and B. P. Haynes, “Pruning artificial neural networks using neural complexity measures”, International Journal of Neural Systems, vol. 18, no. 5, pp. 389-403, 2008.

[14] Lempel A, Ziv J. On the complexity of finite sequences. IEEE Trans Inform Theory, 1976; 22(1): 75-81.

[15] F. Liu and Y. Tang, “Improved Lempel-Ziv Algorithm Based on Complexity Measurement of Short Time Series”, Fourth International Conference on Fuzzy Systems and KnowledgeDiscovery, 2007.

[16] Willi-Hans Steeb, “The Non linear Work Book”- World Scientific Publishing Co. Third Edition, Chapter 2, pp. 107.

[17] A. Kumar and Zhou Yingbo, “Human identification using KnuckleCodes”, In the proc. Of 3rd International Conference on Biometrics: Theory, Applications, and Systems, 2009.

00.05

0.10.15

0.20.25

0.30.35

0.4

0 50 100 150 200

SCGBP

Trai

ning

Erro

r(M

SE)

Iteration

Finger Algorithm Initial database

After feature extracti

on

Detection Accuracy

Mean Error

Left index

BP

400x300 20x10

78.90% 21.10% SCG 96.03% 3.97%

Right Index

BP 74.28% 25.72% SCG 91.65% 8.35%

Left Middle

BP 70.46% 29.54% SCG 80.32% 19.68%

Right Middle

BP 73.76% 26.24% SCG 88.87% 11.13%

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