AMERICAN U NIVERSITY OF BEIRUT FACULTY OF E NGINEERING AND ARCHITECTURE DEPARTMENT OF ELECTRICAL AND COMPUTER E NGINEERING
EECE695C – Adaptive Filtering and Neural Networks
Fingerprint Identification – Project 2 I. Introduction
Fingerprints are imprints formed by friction ridges of the skin and thumbs. They have long been used for identification because of their immutability and individuality. Immutability refers to the permanent and unchanging character of the pattern on each finger. Individuality refers to the uniqueness of ridge details across individuals; the 15 probability that two fingerprints are alike is about 1 in 1.9x10 . However, manual fingerprint verification is so tedious, time consuming and expensive that is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system is widely adopted in many applications such as building or area security and ATM machines [1-2]. Two approaches will be described in this project for fingerprint recognition: • Approach 1: Based on minutiae located in a fingerprint • Approach 2: Based on frequency content and ridge orientation of a fingerprint II. First Approach
Most automatic systems for fingerprint comparison are based on minutiae matching Minutiae are local discontinuities in the fingerprint pattern. A total of 150 different minutiae types have been identified. In practice only ridge ending and ridge bifurcation minutiae types are used in fingerprint recognition. Examples of minutiae are shown in figure 1.
(a) (b) Figure 1. (a) Different minutiae types, (b) Ridge ending & Bifurcation
Many known algorithms have been developed for minutiae extraction based on orientation and gradients of the orientation fields of the ridges [3]. In this project we will adopt the method used by Leung where minutiae are extracted using feedforward artificial neural networks [1]. The building blocks of a fingerprint recognition system are:
Physical Fingerprint
Image Acquisition
Edge Detection
Thinning
Feature Extractor
Classifier Classification Decision
Figure 2. Fingerprint recognition system a) Image Acquisition A number of methods are used to acquire fingerprints. Among them, the inked impression method remains the most popular one. Inkless fingerprint scanners are also present eliminating the intermediate digitization process. In our project we will use the database available for free at University of Bologna (http://bias.csr.unibo.it/fvc2000/) as well as building an AUB database; each one must gather 36 inked fingerprints images from 3 persons (12 images per finger). Fingerprint quality is very important since it affects directly the minutiae extraction algorithm. Two types of degradation usually affect fingerprint images: 1) the ridge lines are not strictly continuous since they sometimes include small breaks ( gaps); 2) parallel ridge lines are not always well separated due to the presence of cluttering noise. The resolution of the scanned fingerprints must be 500 dpi while the size is 300x300. b) Edge Detection An edge is the boundary between two regions with relatively distinct gray level properties. The idea underlying most edge-detection techniques is on the computation of a local derivative operator such as ‘Roberts’, ‘Prewitt’ or ‘Sobel’ operators. In practice, the set of pixels obtained from the edge detection algorithm seldom characterizes a boundary completely because of noise, breaks in the boundary and other effects that introduce spurious intensity discontinuities. Thus, edge detection algorithms typically are followed by linking and other boundary detection procedures designed to assemble edge pixels into meaningful boundaries. For a detailed explanation refer to “ Digital Image Processing ” by Gonzalez, chapters 3 - 4. It is also useful to check the Image Toolbox Demos available in MATLAB. c) Thinning An important approach to representing the structural shape of a plane region is to reduce it to a graph. This reduction may be accomplished by obtaining the skeleton of the region via thinning (also called skeletonizing) algorithm. The thinning algorithm while deleting unwanted edge points should not: • Remove end points. • Break connectedness
•
Cause excessive erosion of the region
For a detailed explanation refer to “ Digital Image Processing ” by Gonzalez, chapter 9. It is also useful to check the following link: http://www.fmrib.ox.ac.uk/~steve/susan/thinning/node2.html
d) Feature Extraction Extraction of appropriate features is one of the most important tasks for a recognition system. The feature extraction method used in [1] will be explained below. A multilayer perceptron (MLP) of three layers is trained to detect the minutiae in the thinned fingerprint image of size 300x300. The first layer of the network has nine neurons associated with the components of the input vector. The hidden layer has five neurons and the output layer has one neuron. The network is trained to output a “1” when the input window in centered on a minutiae and a “0” when it is not. Figure 3 shows the initial training patterns which are composed of 16 samples of bifurcations in eight different orientations and 36 samples of non-bifurcations. The networking will be trained using: • The backpropagation algorithm with momentum and learning rate of 0.3. • The Al-Alaoui backpropagation algorithm. State the number of epochs needed for convergence as well as the training time for the two methods. Once the network is trained, the next step is to input the prototype fingerprint images to extract the minutiae. The fingerprint image is scanned using a 3x3 window given
Figure 3. Training set
(a) (b) (c) (d) Figure 4. Core points on different fingerprint patterns. (a) tented arch, (b) right loop, (c) left loop, (d) whorl e) Classifier After scanning the entire fingerprint image, the resulting output is a binary image revealing the location of minutiae. In order to prevent any falsely reported output and select “significant” minutiae, two more rules are added to enhance the robustness of the algorithm: 1) At those potential minutiae detected points, we re-examine them by increasing the window size by 5x5 and scanning the output image. 2) If two or more minutiae are to close together (few pixels away) we ignore all of them. To insure translation, rotation and scale-invariance, the following operations will be performed: • The Euclidean distance d(i) from each minutiae detected point to the center is calculated. The referencing of the distance data to the center point guarantees the property of positional invariance. • The data will be sorted in ascending order from d(0) to d(N), where N is the number of detected minutiae points, assuring rotational invariance. • The data is then normalized to unity by shortest distance d (0), i.e: dnorm(i) = d(0)/d(i); This will assure scale invariance property. In the algorithm described above, the center of the fingerprint image was used to calculate the Euclidean distance between the center and the feature point. Usually, the center or reference point of the fingerprint image is what is called the “core” point. A core point, is located at the approximate center, is defined as the topmost point on the innermost upwardly curving ridgeline. The human fingerprint is comprised of various types of ridge patterns, traditionally classified according to the decades-old Henry system: left loop, right loop, arch, whorl, and tented arch. Loops make up nearly 2/3 of all fingerprints, whorls are nearly 1/3, and perhaps 5-10% are arches. Figure 4 shows some fingerprint patterns with the core point is marked. Many singularity points detection algorithms were investigated to locate core points, among them the famous “Poincaré” index method [4-5] and the one described in [6]. For simplicity we will assume that the core point is located at the center of the fingerprint image.
After extracting the location of the minutiae for the prototype fingerprint images, the calculated distances will be stored in the database along with the ID or name of the person to whom each fingerprint belongs. The last phase is the verification phase where testing fingerprint image: 1) is inputted to the system 2) minutiae are extracted 3) Minutiae matching: comparing the distances extracted minutiae to the one stored in the database 4) Identify the person State the results obtained (i.e: recognition rate).
III. Second Approach
Most methods for fingerprint identification use minutiae as the fingerprint features. For small scale fingerprint recognition system, it would not be efficient to undergo all the preprocessing steps (edge detection, smoothing, thinning ..etc), instead Gabor filters will be used to extract features directly from the gray level fingerprint as shown figure 5. No preprocessing stage is needed before extracting the features [7].
Physical Fingerprint
Image Acquisition
Feature Extractor
Classifier Classification Decision
Figure 5. Building blocks for the 2
nd
approach
a) Image Acquisition st
The procedure is the same explained in the 1 approach. b) Feature Extractor Gabor filter based features have been successfully and widely applied to face recognition, pattern recognition and fingerprint enhancement. The family of 2-D Gabor filters was originally presented by Daugman (1980) as a framework for understanding the orientation and spatial frequency selectivity properties of the filter. Daugman mathematically elaborated further his work in [8]. In a local neighborhood the gray levels along the parallel ridges and valleys exhibit some ideal sinusoidal shaped plane waves associated with some noise as shown in figure 6 [3].
Figure 6. Sinusoidal plane wave
The general formula of the Gabor filter is defined by:
1 xθ 2 yθ 2 h( x, y ) = exp − 2 + 2 exp(i 2π fxθ ) 2 σ x σ y k
k
k
(1)
Where
• xθ = x cosθ k + y sin θ k k
• xθ = − x sin θ k + y cosθ k k
• • •
f is the frequency of the sinusoidal plane θ is the orientation of the Gabor filter σx and σy are the standard deviations of the Gaussian envelope along the x and y axes The next step is to specify the values of the filter’s parameters; the frequency is calculated as the inverse of the distance between two successive ridges. The number of orientation is specified by “m” whereθ k = π ( k − 1) / m , k = 1, 2, …., m. The standard deviations σx and
σy are determined empirically. In [7] σx = σy = 2 was used, it is advisable to try other values also. Equation (1) can be written in the complex form giving:
h( x, y ) = heven + i.hodd
1 xθ 2 yθ 2 heven ( x, y ) = exp − 2 + 2 cos(2π fxθ ) 2 σ x σ y 1 xθ 2 yθ 2 hodd ( x, y ) = exp − 2 + 2 sin( 2π fxθ ) 2 σ x σ y k
(2)
k
k
k
k
k
Figure 7 shows the filter response in spatial and frequency d omain for a zero orientation.
Figure 7. Gabor filter response Table 1 extracted from [8] described the filter properties in space and spectral domains.
2D Space Domain
2D Frequency Domain
Table 1. Filter properties
The fingerprint print image will be scanned by a 8x8 window; for each block the magnitude of the Gabor filter is extracted with different values of m (m = 4 and m = 8). The features extracted (new reduced size image) will be used as the input to the classifier.
b) Classifier The classifier is based on the k-nearest neighborhood algorithm KNN. “Training” of the KNN consists simply of collecting k images per individual as the training set. The remaining images consists the testing set. The classifier finds the k points in the training set that are the closest to x (relative to the Euclidean distance) and assigns x the label shared by the majority of these k nearest neighbors. Note that k is a parameter of the classifier; it is typically set to an odd value in order to prevent ties. Figure 8 shows how the KNN algorithm works for a two class problem. The KNN query starts at the test point x and grows a spherical region until it encloses k training samples, and it labels the test point by a majority vote of these samples. In this k = 5 case, the test point x would be labeled in the category of the red points [9].
X
Figure 8. The KNN algorithm The last phase is the verification phase where the testing fingerprint image: 1) is inputted to the system 2) magnitude features are extracted 3) perform the KNN algorithm 4) Identify the person State the recognition rate obtained.
c) Suggested enhancement nd
In order to enhance the performance of the 2 approach below is a list of proposed ideas:
• •
• •
Instead of using only the magnitude Gabor filter features, try to use also the phase of the filter [10]. Try to use the Mahalanobis distance given by: D = ( x − m) T C −1 ( x − m) where m is the mean and C is the covariance matrix. Appendix A provides an example of Mahalanobis distance . Try to other classifiers such as backpropagation and ALBP. Indicate the number of layers used as well as the number of neurons. The Gabor filter assumes a sinusoidal plane wave which is not always the case as depicted in figure 9. Try to use the modified Gabor filter described in [11].
Figure 9. A fingerprint with corresponding ridges and valleys.
References [1] W.F. Leung, S.H. Leung, W.H. Lau and A. Luk, "Fingerprint Recognition Using Neural Network", proc. of the IEEE workshop Neural Network for Signal Processing, pp. 226-235, 1991. [2] A. Jain, L. Hong and R. Boler, “Online Fingerprint Verification”, IEEE trans, 1997, PAMI-19, (4), pp. 302-314 [3] L. Hong, Y. Wan, A.K. Jain, “Fingerprint image enhancement: Algorithm and performance evaluation”, IEEE Trans. Pattern Anal. Machine Intell, 1998, 20 (8), 777– 789. [4] Q. Zhang and K. Huang, “Fingerprint classification based on extaction and analysis of singularities and pseudoridges”, 2002 [5] http://www.owlnet.rice.edu/~elec301/Projects00/roshankg/elec301.htm [6] A. Luk, S.H. Leung,”A Two Level Classifier For Fingerprint Recognition”, in Proc. IEEE 1991 International Symposium on CAS, Singapore, 1991, pp. 2625-2628. [7] C.J. Lee and S.D. Wang, “Fingerprint feature extraction using Gabor Filters”, IEE Electronics Letters, vol.35, 1999, pp. 288-290 [8] J.G Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Optical Soc. Amer. 2 (7), 1985, pp. 1160–1169. nd
[9] R. Duda and P. Hart, “Pattern Classification”, Wiley publisher, 2 edition, 2001. [10] M.T. Leung, W.E. Engeler and P. Frank, “Fingerprint Image Processing Using Neural Network”, proc. 10th conf. on Computer and Communication Systems , pp. 582-586, Hong Kong 1990. [11] J. Yang , L. Liu and alt., “A Modified Gabor Filter Design Method for Fingerprint Image Enhancement”, to be published in the Pattern Recognition Letters
Appendix A
