PRESENTATION OUTLINE

SENSOR INTER-OPERABLE FINGERPRINT MATCHING ALGORITHM BASED ON TREE COMPARISON USING RATIOS OF RELATIONAL DISTANCES Abinandhan Chandrasekaran Bhavani Thuraisingham Department of Computer Science University of Texas at Dallas

PRESENTATION OUTLINE • INTRODUCTION and Tutorial based on the research by Anil Jain et al at Michigan State University • Related WORK • PROPOSED FEATURES of our approach • Our ALGORITHM • FUTURE ENHANCEMENTS AND CONCLUSION

INTRODUCTION • Fingerprint Matching is one of the major biometric authentication techniques. • It is more popular because of its ease of operation, cost effectiveness and compatibility between the various formats of data sets. • Academic research has been going on for over 25 years in this area resulting in various advanced algorithms. • Fingerprint matching has its own complexities related to data acquisition, poor quality of the acquired images and finally ability of the algorithm.

Tutorial • What is Finger-Print Scanning • Fingerprint scanning is the acquisition and recognition of a person’s fingerprint characteristics for identification purposes. • This allows the recognition of a person through quantifiable physiological characteristics that verify the identity of an individual. • Methods • There are basically two different types of finger-scanning technology that make this possible. • One is an optical method, which starts with a visual image of a finger. • The other uses a semiconductor-generated electric field to image a finger.

Fingerprint scanning • There are a range of ways to identify fingerprints. • traditional police methods of matching minutiae • straight pattern matching • Ultrasonics • Fingerprint revenues are projected to grow from $144.2m in 2002 to $1,229.8m in 2007. Fingerprint revenues are expected to comprise approximately 30% of the entire biometric technologies • Applications • to access networks and PCs, enter restricted areas, and to authorize transactions. • Deployed in many locations

Basic Terms • Components • Image acquisition systems, image processing components, template generation and matching components, storage components • Surface on which finger is placed is Platen or Scanner • Finger scan module • consists of platen + printed circuit board + standard connector that transmits digitized information to a peripheral or standalone device

Example Technologies • Optical Technology • Oldest technology • Camera registers the image of the fingerprint against a coated glass or plastic platen • Black, gray and white lines • Silicon Technology • Silicon chip embedded in a platen • High image quality • Commercially available since around 1998 • Ultrasound Technology • Transmit acoustic waves to the finger and generating images

Process • Image Acquisition • Measured in terms of dots per inch • Center of the finger print must be placed on the platen • Need appropriate size for platen • Image Processing • Eliminate gray areas from image • Convent gray pixels to black and white pixels • Location of Distinctive Characteristics • Fingerprints consists of ridges and valleys • Swirls, loops, arches, deltas • Ridges and valleys are characterized by irregularities called minutiae • A finger scan image can produce about 15-50 minutiae

Process (Concluded) • Template Creation • Vendors use proprietary algorithms • Depends on the following • Location and angle of a minutiae point • Distance and position of minutiae relative to the core • Type and quality of the minutiae • Need to eliminate sweat, scars, dirt, etc. • Template matching • May depend on the number of minutiae matched

Methods of Finger PrintingMinutiae vs. Pattern matching • Minutiae • Most of the finger-scan technologies are based on minutiae • Pattern Matching • Feature extraction and template generation based on series of ridges as opposed to discrete points • Advantage: Minutiae points affected by wear and tear • Disadvantage: Sensitive to proper placement of finger; large storage for templates • Correlation • Michigan State University of developing correlation based methods

Feature Extraction • The human fingerprint is comprised of various types of ridge patterns • left loop, right loop, arch, whirl, and tented arch. • Loops make up nearly 2/3 of all fingerprints • whirls are nearly 1/3 • 5-10% are arches. • Figure 1 • Source: Book, URL

Feature Extraction (Continued) • Minutiae (Figure 1), the discontinuities that interrupt the otherwise smooth flow of ridges, are the basis for most fingerprint authentication. • Many types of minutiae exist, including dots (very small ridges), islands (ridges slightly longer than dots), ponds or lakes - - - - • The core is the inner point, normally in the middle of the print, around which swirls, loops, or arches center. • Deltas are the points, normally at the lower left and right hand of the fingerprint, around which a triangular series of ridges center. • The ridges are also marked by pores, which appear at steady intervals.

Feature Extraction (Continued) • Once a high-quality image is captured, there are a several steps required to convert its distinctive features into a compact template. • This process, known as feature extraction, is at the core of fingerprint technology. • fingerprint vendor has a proprietary feature extraction mechanism • The image must then be converted to a usable format. • If the image is grayscale, areas lighter than a particular threshold are discarded, and those darker are made black • The ridges are then thinned from 5-8 pixels in width down to one pixel, for precise location of endings and bifurcations.

Feature Extraction (Continued) • Minutiae localization begins with this processed image. • At this point, even a very precise image will have distortions and false minutiae that need to be filtered out • an algorithm may search the image and eliminate one of two adjacent minutiae, as minutiae are very rarely adjacent. • Anomalies caused by scars, sweat, or dirt appear as false minutiae, and algorithms locate any points or patterns that do not make sense • A large percentage of would-be minutiae are discarded in this process.

Feature Extraction (Concluded) • The point at which a ridge ends, and the point where a bifurcation begins, are the most rudimentary minutiae, and are used in most applications. • There is variance in how exactly to situate a minutia point: • whether to place it directly on the end of the ridge, one pixel away from the ending, or one pixel within the ridge ending • Once the point has been situated, its location is commonly indicated by the distance from the core, with the core serving as the 0,0 on an X,Y-axis. • Some vendors classify minutia by type and quality. The advantage of this is that searches can be quicker

Fingerprint Classification • Large volumes of fingerprints are collected and stored everyday in a wide range of applications including forensics, access control, and driver license registration. • An automatic recognition of people based on fingerprints requires that the input fingerprint be matched with a large number of fingerprints in a database (FBI database contains approximately 70 million fingerprints). • To reduce the search time and computational complexity, it is desirable to classify these fingerprints in an accurate and consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database.

Fingerprint Classification (Continued) • Fingerprint classification is a technique to assign a fingerprint into one of the several pre-specified types already established in the literature which can provide an indexing mechanism. • Fingerprint classification can be viewed as a coarse level matching of the fingerprints. • An input fingerprint is first matched at a coarse level to one of the pre-specified types and then, at a finer level, it is compared to the subset of the database containing that type of fingerprints only.

Fingerprint Classification (Concluded) • Michigan State University has developed an algorithm to classify fingerprints into five classes, • whirl, right loop, left loop, arch, and tented arch. • The algorithm separates the number of ridges present in four directions (0 degree, 45 degree, 90 degree, and 135 degree) by filtering the central part of a fingerprint with a bank of Gabor filters. • This information is quantized to generate a FingerCode which is used for classification. • Classification is based on a two-stage classifier which uses a K-nearest neighbor classifier in the first stage and a set of neural networks in the second stage. • The classifier is tested on 4,000 images in the NIST-4 database with about 90% accuracy

2. Related Work • Jain et all used gabor filters to arrive at fingercode representing local and global information. • Nalini K Ratha et all devised a pair of MAG’s [Minutiae Adjacency Graphs] for the base and the input images and compared them to provide a matching score. • Sharath et all proposed their own algorithm called the CBFS [Coupled Breadth First Search] which works upon “k-plet” plottings. [a concept explained in the paper]. • There are also algorithms that work on the entire ridge topology or algorithms that view the image as a graph/tree and so forth.

3. PROPOSED FEATURES • Current algorithms exhibit compatibility / inter-operability at the data set or feature set level only. • They cannot perform matching at the image level when the two to-be compared images are from different sensors or when they are not compatible. • The algorithm proposes to produce satisfactory results when two images obtained from two different sensors are compared. [like capacitive and optical sensor]. • The algorithm also results in decreased FNMR [false non-match rate] when the overlap region [common area] is very less in area.

4. ALGORITHM • Step 1: Minutiae points are obtained from both the input and the base images. [IM and BM hereafter]. • Step 2: A tuple is derived for each of the minutiae point in both IM and BM. [a tuple contains information about the 5 nearest minutiae points, relational distances between them as ratios, hence acts as a minutiae identifier]. • Step 3: An N*N comparison of tuples from both the IM and BM are done to arrive at Candidate Common Point List. [Assume that there are N identified minutiae points in both IM and BM]. • Step 4: A tree connecting the minutiae points is drawn from bottom up in both the BM and IM.

Step 5: The drawn trees from both the images are compared to validate the minutiae points in the Candidate Common Points list. • Step 6: Spurious minutiae are removed from the list at this juncture. • Step 7: While comparing the two trees, for each minutiae in the IM tree, an attempt is made to locate the matching minutiae in the BM tree. When a match is not found, the particular minutiae is removed from the IM tree and the process is continued. • Step 8: This process is iterated till a stage is reached where all minutiae in the IM tree has a corresponding minutiae in the BM tree.

Step 9: Finally when step 8 is satisfied, and if the number of matched minutiae points is greater than [N/2] + 1; [N – total number of minutiae points in the Candidate Common Points List]; the two images are said to be matched. • The following figures provide an overall view of the process: Figure 1 Two images of the same finger from two different sensors

Figure 2 Two images at different instances; minutiae points inside squares are available in both images, while those in circles are unique to each image. Figure 3 Tree structures drawn in both images [ points not in tree are identified as spurious minutiae points].

5. FUTURE ENHANCEMENTS AND CONCLUSION • The algorithm produced satisfactory results with cumulative acceptance rate of 96.43% when images from same sensor were used and an acceptance rate of 88.05% when images from different sensors were used. • The overall FAR [false acceptance rate] was found to be 0.35%. • The results could be improved by increasing additional parameters that could aid in the matching process. • As a future enhancement, this algorithm could be made to work on top of existing algorithms as an additional level of authentication.

PRESENTATION OUTLINE