DEVELOPMENT OF A HYBRID PLATFORM FOR THUMBPRINT FORENSICS

Transcription

1 DEVELPMENT F A HYRID PLATFRM FR THUMPRINT FRENSICS luwole Charles Akinyokun 1, Cleopas fficer Angaye, abatunde Gabriel Iwasokun 1 1 Department of Computer Science, Federal University of Technology, Akure, Nigeria National Information Technology Development Agency (NITDA), Abuja, Nigeria akinwole003@yahoo co.uk; cangaye@hotmail.com; maxtunde@yahoo.com ASTRACT. Thumbprint has remained the most commonly used technique for biometric identification in the world today. Research activities on the improvement of the theory and practice of thumbprint forensics have, therefore continued to be a major task in the world today. The research which is reported in this paper takes a study of the features of the existing and popular research activities on the theory and practice of thumbprint forensics, analyzed their Strengths, Weaknesses, pportunities and Threats (SWT) and proposed a hybrid system for thumbprint forensics which integrates the strengths and opportunities. The hybrid system is characterized by an architecture which is composed of database of thumbprint profile, data mining engine and decision support engine. The data mining engine is composed of thumbprint image enhancement, thumbprint minutiae extraction, database of thumbprint minutiae extraction, thumbprint pattern recognition and thumbprint pattern matching. The decision support engine is composed of error detection and error correction subsystems. Keywords: Forensics, iometrics, Thumbprint, Minutiae, Segmentation, Normalization, inarization 1 INTRDUCTIN Thumbprints are the results of minute ridges and valleys found on the thumb of every person. It is an impression of the friction ridges of all or any part of the thumb. A friction ridge is a raised portion of the epidermis on the palmar (palm and thumbs) or plantar (sole and toes) skin, consisting of one or more connected ridge units of friction ridge skin. Thumbprint is also an impression of the coetaneous ridges of the fleshy distal portion of a thumb, which may be obtained by applying ink and pressing the thumb on paper. Each print has an exclusive owner, and there has never been two individuals recorded with the same print [1, ]. The ridges of thumb form patterns of loops, whorls and arches [3, 4, 5, 6, 7]. 183

2 Figure 1.1: Comparative Analysis of iometrics Identifications The result of the survey conducted by the International iometric Group (IG) in 004 as presented in Figure 1.1 confirms that thumbprint identification is a more widely used technique of biometric identification in the world today [8].The principle and practice of thumbprint forensics are concerned with pattern recognition and matching. In [9], the features of the existing and popular techniques of pattern recognition and matching; namely: template technique, prototype technique, feature based technique and computational technique were presented. In [10, 11, 1, 13, 14, 15, 16, 17, 18, 19], thumbprint forensics which adopts some of the techniques for pattern recognition and matching were reported. The features of the research activities of these listed authorities were studied and their strengths were integrated to form the basis of the proposed hybrid system for thumbprint forensics. The architecture of the hybrid system is composed of the database of thumbprint profile, data mining engine and decision support engine. The data mining engine is composed of thumbprint image enhancement, thumbprint minutiae extraction, database of thumbprint minutiae extraction, thumbprint pattern recognition and thumbprint pattern matching. The decision support engine is composed of error detection and error correction subsystems. ARCHITECTURE F THUMPRINT FRENSICS The conceptual diagram of the proposed architecture of thumbprint forensics is presented in Figure.1. It is composed of database of thumbprint profile, data mining engine and decision support engine. 184

3 P Acquisition of Thumbprints Acquisition of other iometric Data Database of Thumbprint Image Enhancement Image Segmenta Normaliza tion Gabor Filter inarizati on/thinn ing Thumbprint Data Mining Engine Thumbprint Minutiae Mathematic al Model 3CF Databas e of Extracte d Thumbprint Pattern Similarity Score Calculati on Dissimila r Formula Thumbprint Pattern Matching Multipath method Minimu m Cost Flow method 3CF: Chain Coded Contour Following FMR: False Match Rate Decision Support Engine Error Error Detectio Correctio n FMR Cognitive n Filter FNMR Emotion al Filter Decision Making Figure.1: Architecture of Thumbprint Forensics 185

4 .1 Database of Thumbprint Profile The building of thumbprint profile database starts with the acquisition of all relevant data and the major tasks are the following: Thumbprint enrollment. Acquisition of facial image of the person whose thumbprint is being captured. Collection of other relevant biometric data. The success of thumbprint processing depends greatly on the quality of the thumbprint image. Consequently, a high quality digital thumbprint scanner that will produce low noise and low contract deficiency images is used for the enrollment. A digital camera with a high resolution and pixel configurations is used for the acquisition of facial images. ther relevant biometric data that are desirable for the verification and validation of a person s unique identification are names, signature, complexion, height, colour of eyes (Irish), tribe, date of birth, age, blood group, marital status, thumbprint pattern type and features (end point, bifurcation point or island, cross over, lake, independent ridge and so on).. Data Mining Engine The data mining engine is responsible for the extraction of the needed features from the captured thumbprints and the processing of these features to obtain aggregate data which could assist in taking notable decisions on the degree of resemblance or similarity of the captured thumbprints. The data mining engine is composed of the following: Thumbprint image enhancement. Thumbprint minutiae extraction. Database of extracted thumbprint minutiae. Thumbprint pattern recognition. Thumbprint pattern matching...1 Thumbprint Image Enhancement The process of thumbprint image enhancement has the following phases: a. Image Segmentation: It is the process of separating the foreground regions in the image from the background regions. The foreground regions correspond to the clear thumbprint area containing the ridges and valleys, which is the area of interest. The background corresponds to the regions outside the borders of the thumbprint area, which do not contain any valid thumbprint information. When minutiae extraction algorithms are applied to the background regions of an image, it results in the extraction of noisy and false minutiae. Thus, segmentation 186

5 is employed to discard these background regions for the purpose of obtaining fine and reliable extraction of thumbprint minutiae. In a thumbprint image, the background regions generally exhibit a very low greyscale variance value, whereas the foreground regions have a very high variance. Hence, a method based on variance threshold is used to perform the segmentation. Firstly, the image is divided into blocks and the grey-scale variance is calculated for each block in the image. If the variance is less than the global threshold, then the block is assigned to a background region; otherwise, it is assigned to the foreground region. The grey-level variance for a block of size W x W is defined as: 1 V ( k) W W 1W 1 i 0 j 0 ( I ( i, j) M ( k)) (.1) where V (k) is the variance for block k, I(i, j) is the grey-level value at pixel (i, j), and M(k) is the mean grey-level value for the block k. b. Image Normalization: It is used to standardize the intensity values in an image by adjusting the range of grey-level values so that it lies within a desired range of values. If I(i, j) represents the grey-level value at pixel (i, j), and N(i, j ) represents the normalized grey-level value at pixel (i, j ). The normalized image is defined as: M o N( i, j) M o V o V o ( I ( i, j) M V ( I( i, j) M ) V ) if I( i, j) M if otherwise...(.) where M and V are the estimated mean and variance of I(i, j) respectively, M 0 and V 0 are the values of the desired mean and variance, respectively. Normalization does not change the ridge structures in a thumbprint. It is performed to standardize the dynamic levels of variation in grey-level values, which facilitates the processing of subsequent image enhancement stages. 187

6 c. Application of Gabor Filter: Gabor filter which is a very useful tool for analysis in spatial or frequency domain [16] is adopted in this phase. It is applied for the enhancement of the segmented thumbprint images. It has both frequency selective property and orientation selective property. It also has optimal joint resolution in both spatial and frequency domain. It is used to further improve the segmented thumbprint image by removing noise and preserving the true ridge and valley structures. The general form of Gabor filter is: / x / 1 y G( x, y : f, ) exp x y / cos(fx )...(.3) where f is the frequency of the sinusoidal plane wave along the direction θ from the x-axis, and δ x and δ y are the space constants along x and y axes respectively. The values of the space constants δ x and δ y for the Gabor filters were empirically determined as each is set to about half the average inter-ridge distance in their respective direction. In addition, x = xsin + ycos and y = xcos + ysin. d. Image inarization/thininng: The image obtained from the application of the Gabor filter is binarized or thinned to obtain its best performance threshold. The tsu method of image binarization proposed in [19] is employed. The tsu Method sets the threshold (T) for making each cluster as tight as possible, thereby minimizing their overlap. To determine the actual value of T, the following operations are performed on a set of presumed threshold values: i. The pixels are separated into two clusters according to the threshold. ii. The mean of each cluster are determined. iii. The difference between the means is squared. iv. The product of the number of pixels in one cluster and the number in the other is determined. The success of these operations depends on the difference between the means of the clusters. The optimal threshold is the one that maximizes the between-class variance or, conversely, the one that minimizes the within-class variance. The within-class variance of each of the cluster is calculated as: 188

7 within where T n ( T ) p( i)...(.5) i0 N 1 n ( T ) p( i)...(.6) it ( T ) the var iance of the pixels in the background ( below) threshold ( T ) n ( T ) ( T ) T 1 n ( T ) ( T )...(.4) the var iance of the pixels in the foreground ( above) threshold between p(i) is the pixel value at location i, N is the intensity level and [0,N 1] is the range of intensity levels. The between-class variance, which is the difference between the within-class variance and the total variance of the combined distribution, is then obtained from: T T n ( T ) ( T ) n ( T ) ( T )...(.7) within where σ is the combined variance and µ is the combined mean. The betweenclass variance is simply the weighted variance of the cluster means themselves around the overall mean. Substituting µ = n(t)µ(t) +n(t)µ(t) and simplifying further, the result is: between T n ( T ) n ( T ) ( T ) ( T )...(.8) Using recurrence relations, the between-class variance is updated as each threshold is successfully tested by using the following formula: n n ( T 1) ( T 1) ( T 1) ( T 1) n n ( T ) n ( T ) n ( T ) n ( T ) ntt n ( T 1) ( T ) n n T T ( T ) n T ( T 1) T...(.9)...(.10)...(.11)...(.1) 189

8 .. Thumbprint Minutiae Extraction The following alternative methods are proposed for the extraction of thumbprint: a. Mathematical model: A mathematical model that hinges on the assumption that the pixel is on a thinned image (eight-connected) and has a value of one and zero otherwise as presented in [11] is used. The mathematical model uses (x, y) to denote a pixel on a thinned ridge and N 0, N 1,, N 7 to denote its neighbours. Then pixel (x, y) is considered as a ridge ending if: 7 N i i0 1 (.13) and as a ridge bifurcation if: 7 N i i0 (.14) With these steps, the algorithm is to identify and extract most of the minutiae present in the thinned image. The extracted thumbprint minutiae will be stored in a database before they are subjected to pattern recognition. b. Chain Code ased Minutiae Extraction: The conventional minutiae extraction algorithms are based on thinning and iterative process which are computationally expensive and produce artifacts such as spurs and bridges. To avoid this problem, a chain coded contour tracing method is proposed. In the chain code based minutiae extraction, chain coded contour following (3CF) method where the ridges and valleys of a thumbprint in a -dimentional representation are represented in contours is used for thumbprint minutiae extraction [17]. A wide range of information such as curvature, direction and length are derived from the contours. To obtain the minutiae from the contours, the ridges are traced consistently in anti-clockwise direction for the purpose of exhibiting the following features: i. Minutiae points which are considered as places where the contour has a significant turn. ii. Ridge end which is a significant left turn in the contour iii. ifurcation which is a significant right turn in the contour. 190

9 The turning direction is determined by taking into consideration the sign of the cross product of the incoming and outgoing vectors at each point. The sign of the cross product is obtained from: sgn( Pin x Pout sgn( x1 y x y1)...(.15) where P in is the incoming vector, P out is the outgoing vector and x 1 y and x y 1 define the pixel locations of the vectors. The product is right handled if the sign is positive and left handled if the sign is negative as shown in Figure.. (a) (b) (c) Figure.: (a) Minutiae marked by significant turn in the contour (b) Left turn (c) Right turn The turn is only significant if x 1 y 1 + x y is less than or equals to a small threshold value T. No matter what type of turning points are detected, if the angle between the leading in and out of a vector for the interested point is greater than 90 o, then, the threshold T is chosen to have a small value. The angle can be calculated by using dot product of the two vectors given by: arccos P. in Pout P. in Pout (.16) The extracted minutiae will be stored in a database before they are subjected to pattern recognition. 191

10 ..3 Database of Extracted Thumbprint Minutiae A database of the extracted minutiae is created to serve as repository of the minutiae that were extracted from a thumbprint. The minutiae database assumes a relational model to give room for defining the relationships among the minutiae from different thumbprints. It is also used for effective and dynamic minutiae storage pattern. The dynamic nature of the database gives room for an open end so that continuous growth of the database is not prevented. The data are stored in tables of related entities such as thumbprint identification number, thumbprint type, type of minutiae and number of extracted minutiae. The data obtained for each thumbprint is assigned a unique identification number that promote collective and effective identification as well as smooth reading during subsequent operations. Let S represents the similarity score; Let height c represents the height of combined fingerprint; Let width c represents the width of combined fingerprint; Let max h represents the maximum possible height; Let max w represents the maximum possible width; Let T m be an integer-valued threshold; If (N < 7 and (height c > max h or width c > max w )) then S = 0; Else If ( a < 5) then a = 5; End if If ( b < 5) then b = 5; End if 19

11 ..4 Thumbprint Pattern Recognition The following two methods are proposed for the pattern recognition of the extracted thumbprint minutiae: a. Similarity Score Calculation: The similarity score will be generated to obtain the degree of similarity of thumbprint minutiae extracted from the thumbprint images. To achieve this objective, minutiae based query I and reference R thumbprint databases are formulated. The following information is then used in the computation of the similarity scores: i. n: the number of matched feature points; ii. size I : the number of feature points on the query I; iii. size R : the number of feature points on the reference R; iv. I : the number of feature points in the overlapping area of query I; v. R : the number of feature points in the overlapping area of reference R; vi. S avg : the average feature distance of all the matched features. Using features such as ridge flows, singular points and scars, the similarity scores for the minutiae-based system is calculated from: n /(size I x size R ) (.17) The details of the heuristic rule for generating similarity scores are presented in Figure.3. b. Dissimilar Score Calculation: It is characterized by dissimilar algorithm for recognizing and identifying thumbprint images using the distances between mid point and the core point of the thumbprint for pattern recognition. Image existing junction points were classified as bi-junctions, tri-junctions and quad-junctions. The classification is also based on the number of lines passing through each image. Distances between these points and the image core were computed and used as recognition features. To minimize the size of the adopted feature data, only the nearest 10-junctions to the core of each type would be considered as proposed in [10]. The following were strictly observed: i. If 10-junctions of any type have not been found then, distances of only those found are calculated and recorded while the rest of the 10-values are defined as zeros. 193

12 ii. To avoid any image rotational effect, the measured features were arranged in a descending order (that is from farthest to nearest to the core point). The following dissimilar formula is used to measure the distances between matched images: DIS 34 P( i) I( i) %( ) 1 ( ) S i i P i...(.18) where, P(i) and I(i) represent preserved and identified image distances respectively. S%(i) are weighting factors assigned for measured matching features as follows: i. S% = 1% for distances of maximum lines in the 4 directions. ii. S% = % for i-junction points. iii. S% = 3% for Tri-junction points. iv. S% = 4% for Quad-junction points. If all the p(i) elements were in existence, the maximum dissimilar coefficient (DIS) will not exceed 94%. Therefore, normalized Cross-Correlation Coefficient (CR) is computed and used to measure the similarity between matched images by using the formula: CR = 1 (DIS/94) (.19) From this formula, in case of exact similar matched images the dissimilar value will be DIS = 0 and, consequently, the cross-correlation will be Thumbprint Pattern Matching: The proposed pattern matching subsystem is expected to take any of the following two methods for its thumbprint pattern matching tasks: a. Multi-path Matching Method: A multi-path matching algorithm includes the following two matching methods: i. rute-force matching 194

13 ii. Secondary feature matching rute force matching is for small number of minutiae query and reference partial thumbprints while secondary feature matching is for the relative larger number of minutiae in query and reference thumbprints. In the multi-path approach to pattern matching of thumbprints shown in Figure.4, a scenario based on the number of minutiae on the query I and the reference R thumbprints in the thumbprint recognition system is used. M and N are taken as the number of minutiae on query and reference thumbprints respectively where α represents a pre-defined threshold value. The brute-force matching system is used if any of the following conditions is met: i. oth number of minutiae on I and R are less than the predefined value α; ii. Either I or R has number of minutiae less than α; and iii. oth I and R contain more than α minutiae. Query Fingerprin t l Minutiae Extraction M > α and Yes Secondary Feature Matching Template Database R No M <= α and Yes rute-force Matching Result No Secondary Feature Extraction No Has a match? Yes Figure.4: Flowchart of rute-force Matching Algorithm rute-force technique will examine all the possible solutions and select the match with the highest number of matches. Each minutia point in R and I are used as reference points. If none of the above conditions is met, secondary feature matching is adopted. Choosing the appropriate matching methods in terms of speed and accuracy is dependent on an empirical threshold (α). The technique involves the following steps: i. Working directly on the minutiae information, all possible correspondences between the minutiae on query I and reference R thumbprints are determined. ii. For each minutiae p i (x i, y i,i ) on I and q j (x j, y j, j ) on R, p i and q j were taken as the matched reference points. 195

14 iii. ther matched minutiae in the polar coordinate system were subsequently determined. b. Minimum Cost Flow (MCF) Method: It is used to provide the matching scenarios with the least cost in terms of minutia detection and matching times. Emphasis will be on the maximum number of matches and the minimum cost. Using MCF technique for matching the feature points on any two thumbprints is equivalent to finding the correspondences between the feature points. When there are two sets of feature points from different thumbprint images, say, I and R and they are already aligned with respect to a pair of reference points in each image, an extra point (node), say the source s, is added into the set of I and add the point (node), say sink (target) t to the set of R. The links (edges) between nodes will be set up by obeying the following rules: i. ne and only one link exists, that connects s to every point in the first set. ii. ne and only one link exists, that connects t to every point in the second set. iv. There is no link between the points within the same set. v. There is exactly one link between every point in first set and every point in the second set. vi. Every link is associated with a capacity and a cost. The cost matrix C i,j which represents the costs of the edge between I and R is defined by: C i, j = dist (I i, R j ) where 1 i N i and 1 j N R given that N i and N R are the numbers of feature points on R and I respectively and also, I i and R j are the feature point on I and R respectively. The function dist(a,b) is the distance measure of two feature points, a and b on I and R, respectively. For efficiency, the edge between I i and R j are removed if I i is not in the tolerance area of R j..3 Decision Support Engine.(.0) The decision support engine of the proposed system is to aid the user in making good decisions based on findings from the thumbprint minutiae pattern recognition and matching. This unit comprises of two subcomponents, namely: Error Detection and Error Correction 196

15 a. Error Detection: The error detection subcomponent is concerned with identifying the errors that feature during thumbprint pattern recognition and matching. Thumbprint pattern recognition and matching system, like any other biometric system, is susceptible to the following two types of error: i. Mistaking thumbprint measurements from two different persons described as false match. ii. Mistaking two thumbprint measurements from same person to be from two different persons describe as false non-match. Due to variations in the image quality, intra-class variations in the thumbprint capture devices, limitations of thumbprint image analysis systems, enhancement methods, feature detection algorithms and matching algorithms, a genuine individual could be mistakenly rejected. Similarly, an imposter individual could be falsely accepted. The rate of mistaking thumbprint measurements from two different persons is called False Match Rate (FMR) while the rate of mistaking two thumbprint measurements from same person to be from two different persons is described as False Non-Match Rate (FNMR). In the system being proposed, there is a tradeoff between the FMR and FNMR. oth FMR and FNMR are functions of the predefined system threshold t. If t is decreased to make the system more tolerant to input variation (that is, match thumbprints on the basis of minimum number of common features) and noise, then FMR increases. n the other hand, if t is raised to make the system more reliable (that is, match thumbprints on the basis of maximum number of common features), then FNMR increases accordingly. The errors in thumbprint pattern recognition and matching as they apply to the system being proposed can be formulated as follows: If the stored thumbprint template of the first person is represented by X I and the enrolled thumbprint for recognition is represented by X Q, the null and alternate hypotheses are: H 0 : enrolled thumbprint X Q does not come from the same person as the template X I H I : enrolled thumbprint X Q comes from the same person as the template X I The following are therefore the associated decisions: D 0 : the template thumbprint X I and the enrolled thumbprint X Q do not come from the same person 197

16 D I : the template thumbprint X I and the enrolled thumbprint X Q are from the same person The following will guide these decisions: If the matching score (X Q, X I ) is less than the system threshold t, then D 0 else D I FMR is the probability of false match while FNMR is the probability of false non-match and given as:.(.1) FMR = P(D I H ) FNMR = P(D 0 H I ) 3.18.(.) 1 FNMR would be defined as the power of the hypothesis test. Therefore, to evaluate the accuracy of the thumbprint pattern recognition system, scores from several images of same thumb (which is the distribution p(s(x Q, X I ) H I )) as well as scores from several images of different thumb (which is the distribution p(s(x Q, X I ) H )) will be collected. FMR and FNMR are then computed from: FMR p ( S ( X Q, X I ) H ) ds t...(.3) FNMR t p( S ( X Q, X I ) H I ) ds...(.4 ) b. Error Correction: The error correction system is characterized by a cognitive filter and an emotional filter. The cognitive filter takes the output of the error detecting unit as input and applies the objective rules in the domain of thumbprint pattern recognition and matching with a view to ranking the alternative output reports of the error detecting unit. The emotional filter takes the output of the cognitive filter as input and applies the subjective rules in the domain of thumbprint pattern recognition and matching with a view to ranking the alternative output reports of the cognitive filter. The final goal is to filter the cognitive and emotional factors that may inhibit sound decision-making on the degree of similarity between two or more thumbprints. During cognitive filtering of the results of thumbprint matching, judgment or decision on errors is based on objective considerations of information existing 198

17 between the thumbprints. This involves a genuine and close observation of empirical data from the thumbprints. Such empirical data could include data from both inductive and deductive logic, similarity score pattern and information from the thumbprints such as the location, the orientation and the position of each print. bjective consideration of errors by inductive and deductive logic involves the use of experience which is the long term residual knowledge regarding past observations from thumbprint matching to fix the errors. It also involves the use of physical evidence such as thumbprint pattern and ridge information, investigative information, and other documentary and testimonial evidence. During emotional filtering of the results of thumbprint matching, judgment or decision made on the detected errors is based on sentiments rather than principles. In this case, the results from thumbprint matching are subjected to emotional considerations before a final decision is taken. For instance the reliability of the recording instrument (scanner), the efficiency of the matching algorithms, the integrity or fairness of the thumbprint pattern recognition and matching team and other personal interests are factors that could determine the final decision on the errors. 3. CNCLUSINS The process of recognizing a pattern involves the identification of a complex arrangement of sensory stimuli. The four distinct techniques of the cognitive perspective of pattern recognition are the Template-Matching technique, Prototype-Matching technique, Feature-ased technique and Computational technique. Each of these techniques has been found to have some strengths, weaknesses, opportunities and threats. Existing and popular research activities have been found to adopt and integrate only a few of these techniques as the driver of their thumbprint forensics. An attempt has been made, in this paper, to present a framework for cognitive perspective of pattern recognition of thumbprint which integrates the strengths of the above listed four techniques and emotional perspective mechanism which is contained in the decision support engine. The experimental study of the proposed hybrid system for thumbprint forensics is currently being carried out with a view to demonstrating its practical function. The environment of the experimental study is characterized by LINUX perating System as the platform, Microsoft Access Database Management System as backend engine and MATLA as front-end engine. References 1. Eckert W. G.: Introduction to Forensic Science ; New York: Elsevier, 1996, pp FIDIS: Forensic Implications of Identity Management Systems, Technical report by the FIDIS consortium - EC Contract No , 006, pp

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