Fingerprint Identification Project 2

Transcription

1 I. Introduction AMERICAN UNIVERSITY OF BEIRUT FACULTY OF ENGINEERING AND ARCHITECTURE DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING EECE695C Adaptive Filtering and Neural Networs Fingerprint Identification Project Fingerprints are imprints formed b friction ridges of the sin and thumbs. The have long been used for identification because of their immutabilit and individualit. Immutabilit refers to the permanent and unchanging character of the pattern on each finger. Individualit refers to the uniqueness of ridge details across individuals; the probabilit that two fingerprints are alie is about 1 in However, manual fingerprint verification is so tedious, time consuming and epensive that is incapable of meeting toda s increasing performance requirements. An automatic fingerprint identification sstem is widel adopted in man applications such as building or area securit and ATM machines [1-]. Two approaches will be described in this project for fingerprint recognition: Approach 1: Based on minutiae located in a fingerprint Approach : Based on frequenc content and ridge orientation of a fingerprint II. First Approach Most automatic sstems for fingerprint comparison are based on minutiae matching Minutiae are local discontinuities in the fingerprint pattern. A total of 150 different minutiae tpes have been identified. In practice onl ridge ending and ridge bifurcation minutiae tpes are used in fingerprint recognition. Eamples of minutiae are shown in figure 1. (a) (b) Figure 1. (a) Different minutiae tpes, (b) Ridge ending & Bifurcation

2 Man nown algorithms have been developed for minutiae etraction based on orientation and gradients of the orientation fields of the ridges [3]. In this project we will adopt the method used b Leung where minutiae are etracted using feedforward artificial neural networs [1]. The building blocs of a fingerprint recognition sstem are: Phsical Fingerprint Image Acquisition Edge Detection Thinning Feature Etractor Classifier Classification Decision Figure. Fingerprint recognition sstem a) Image Acquisition A number of methods are used to acquire fingerprints. Among them, the ined impression method remains the most popular one. Inless fingerprint scanners are also present eliminating the intermediate digitization process. In our project we will use the database available for free at Universit of Bologna ( as well as building an AUB database; each one must gather 36 ined fingerprints images from 3 persons (1 images per finger). Fingerprint qualit is ver important since it affects directl the minutiae etraction algorithm. Two tpes of degradation usuall affect fingerprint images: 1) the ridge lines are not strictl continuous since the sometimes include small breas (gaps); ) parallel ridge lines are not alwas well separated due to the presence of cluttering noise. The resolution of the scanned fingerprints must be 500 dpi while the size is b) Edge Detection An edge is the boundar between two regions with relativel distinct gra level properties. The idea underling 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 piels obtained from the edge detection algorithm seldom characterizes a boundar completel because of noise, breas in the boundar and other effects that introduce spurious intensit discontinuities. Thus, edge detection algorithms tpicall are followed b lining and other boundar detection procedures designed to assemble edge piels into meaningful boundaries. For a detailed eplanation refer to Digital Image Processing b Gonzalez, chapters 3-4. It is also useful to chec the Image Toolbo 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 ma be accomplished b obtaining the seleton of the region via thinning (also called seletonizing) algorithm. The thinning algorithm while deleting unwanted edge points should not: Remove end points. Brea connectedness

3 Cause ecessive erosion of the region For a detailed eplanation refer to Digital Image Processing b Gonzalez, chapter 9. It is also useful to chec the following lin: d) Feature Etraction Etraction of appropriate features is one of the most important tass for a recognition sstem. The feature etraction method used in [1] will be eplained below. A multilaer perceptron (MLP) of three laers is trained to detect the minutiae in the thinned fingerprint image of size The first laer of the networ has nine neurons associated with the components of the input vector. The hidden laer has five neurons and the output laer has one neuron. The networ 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 networing will be trained using: The bacpropagation algorithm with momentum and learning rate of 0.3. The Al-Alaoui bacpropagation algorithm. State the number of epochs needed for convergence as well as the training time for the two methods. Once the networ is trained, the net step is to input the prototpe fingerprint images to etract the minutiae. The fingerprint image is scanned using a 33 window given Figure 3. Training set

4 (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 binar image revealing the location of minutiae. In order to prevent an falsel 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-eamine them b increasing the window size b 55 and scanning the output image. ) If two or more minutiae are to close together (few piels awa) 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 propert 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 unit b shortest distance d (0), i.e: d norm (i) = d(0)/d(i); This will assure scale invariance propert. 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. Usuall, the center or reference point of the fingerprint image is what is called the core point. A core point, is located at the approimate center, is defined as the topmost point on the innermost upwardl curving ridgeline. The human fingerprint is comprised of various tpes of ridge patterns, traditionall classified according to the decades-old Henr sstem: left loop, right loop, arch, whorl, and tented arch. Loops mae up nearl /3 of all fingerprints, whorls are nearl 1/3, and perhaps 5-10% are arches. Figure 4 shows some fingerprint patterns with the core point is mared. Man singularit points detection algorithms were investigated to locate core points, among them the famous Poincaré inde method [4-5] and the one described in [6]. For simplicit we will assume that the core point is located at the center of the fingerprint image.

5 After etracting the location of the minutiae for the prototpe 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 sstem ) minutiae are etracted 3) Minutiae matching: comparing the distances etracted minutiae to the one stored in the database 4) Identif 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 sstem, it would not be efficient to undergo all the preprocessing steps (edge detection, smoothing, thinning..etc), instead Gabor filters will be used to etract features directl from the gra level fingerprint as shown figure 5. No preprocessing stage is needed before etracting the features [7]. Phsical Fingerprint Image Acquisition Feature Etractor Classifier Classification Decision Figure 5. Building blocs for the nd approach a) Image Acquisition The procedure is the same eplained in the 1 st approach. b) Feature Etractor Gabor filter based features have been successfull and widel applied to face recognition, pattern recognition and fingerprint enhancement. The famil of -D Gabor filters was originall presented b Daugman (1980) as a framewor for understanding the orientation and spatial frequenc selectivit properties of the filter. Daugman mathematicall elaborated further his wor in [8]. In a local neighborhood the gra levels along the parallel ridges and valles ehibit some ideal sinusoidal shaped plane waves associated with some noise as shown in figure 6 [3].

6 Figure 6. Sinusoidal plane wave The general formula of the Gabor filter is defined b: 1 h(, ) ep ep( iπf ) σ σ = + (1) Where = cos + sin = sin + cos f is the frequenc of the sinusoidal plane is the orientation of the Gabor filter σ and σ are the standard deviations of the Gaussian envelope along the and aes The net step is to specif the values of the filter s parameters; the frequenc is calculated as the inverse of the distance between two successive ridges. The number of orientation is specified b m where = π ( 1) / m, = 1,,., m. The standard deviations σ and σ are determined empiricall. In [7] σ = σ = was used, it is advisable to tr other values also. Equation (1) can be written in the comple form giving:

7 ) sin( 1 ep ), ( ) cos( 1 ep ), (. ), ( f h f h h i h h odd even odd even π σ σ π σ σ + = + = + = () Figure 7 shows the filter response in spatial and frequenc domain for a zero orientation. Figure 7. Gabor filter response Table 1 etracted from [8] described the filter properties in space and spectral domains. D Space Domain D Frequenc Domain Table 1. Filter properties

8 The fingerprint print image will be scanned b a 88 window; for each bloc the magnitude of the Gabor filter is etracted with different values of m (m = 4 and m = 8). The features etracted (new reduced size image) will be used as the input to the classifier. b) Classifier The classifier is based on the -nearest neighborhood algorithm KNN. Training of the KNN consists simpl of collecting images per individual as the training set. The remaining images consists the testing set. The classifier finds the points in the training set that are the closest to (relative to the Euclidean distance) and assigns the label shared b the majorit of these nearest neighbors. Note that is a parameter of the classifier; it is tpicall set to an odd value in order to prevent ties. Figure 8 shows how the KNN algorithm wors for a two class problem. The KNN quer starts at the test point and grows a spherical region until it encloses training samples, and it labels the test point b a majorit vote of these samples. In this = 5 case, the test point would be labeled in the categor 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 sstem ) magnitude features are etracted 3) perform the KNN algorithm 4) Identif the person State the recognition rate obtained. c) Suggested enhancement In order to enhance the performance of the nd approach below is a list of proposed ideas:

9 Instead of using onl the magnitude Gabor filter features, tr to use also the phase of the filter [10]. T 1 Tr to use the Mahalanobis distance given b: D = ( m) C ( m) where m is the mean and C is the covariance matri. Appendi A provides an eample of Mahalanobis distance. Tr to other classifiers such as bacpropagation and ALBP. Indicate the number of laers used as well as the number of neurons. The Gabor filter assumes a sinusoidal plane wave which is not alwas the case as depicted in figure 9. Tr to use the modified Gabor filter described in [11]. Figure 9. A fingerprint with corresponding ridges and valles.

10 References [1] W.F. Leung, S.H. Leung, W.H. Lau and A. Lu, "Fingerprint Recognition Using Neural Networ", proc. of the IEEE worshop Neural Networ for Signal Processing, pp. 6-35, [] A. Jain, L. Hong and R. Boler, Online Fingerprint Verification, IEEE trans, 1997, PAMI-19, (4), pp [3] L. Hong, Y. Wan, A.K. Jain, Fingerprint image enhancement: Algorithm and performance evaluation, IEEE Trans. Pattern Anal. Machine Intell, 1998, 0 (8), [4] Q. Zhang and K. Huang, Fingerprint classification based on etaction and analsis of singularities and pseudoridges, 00 [5] [6] A. Lu, S.H. Leung, A Two Level Classifier For Fingerprint Recognition, in Proc. IEEE 1991 International Smposium on CAS, Singapore, 1991, pp [7] C.J. Lee and S.D. Wang, Fingerprint feature etraction using Gabor Filters, IEE Electronics Letters, vol.35, 1999, pp [8] J.G Daugman, Uncertaint relation for resolution in space, spatial frequenc, and orientation optimized b two-dimensional visual cortical filters. J. Optical Soc. Amer. (7), 1985, pp [9] R. Duda and P. Hart, Pattern Classification, Wile publisher, nd edition, 001. [10] M.T. Leung, W.E. Engeler and P. Fran, Fingerprint Image Processing Using Neural Networ, proc. 10th conf. on Computer and Communication Sstems, pp , Hong Kong [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

11 Appendi A

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