January 21, Fingerprint Identification BIOM 426 Instructor: Natalia A. Schmid

January 21, Introduction Applications: - law enforcement - access to computer, network, bank-machine, car, home - security applications (US Visit)

January 21, Introduction Factors in favor of fingerprint applications: small and inexpensive capture devices (about 100 USD); fast computing hardware; recognition rate meets the needs of many applications (about 1 sec); increasing number of networks and Internet transactions; awareness of the need for ease-of-use as an important component of reliable security well accepted by public

January 21, History Use of fingerprints for identification since 7000 to 6000 BC by ancient Assyrians and Chinese (prints on pottery, clay, bricks). Fingerprinting of criminals for identification ~ Babylon around BC.

January 21, History In the mid-1800's two facts were established: (i) no two fingerprints have the same ridge pattern and (ii) fingerprint pattern have good permanence. Use of fingerprints for criminal identification in Argentina in Henry's fingerprint classification system was introduced in Computer processing began in 1960s with introduction of computer hardware. Since 1980s fingerprints are used in non-criminal applications (due to personal computers and optical scanners). Personal use ~ due to introduction of inexpensive capture devises and reliable matching algorithms.

January 21, Feature Types The lines that flow in various patterns across fingerprints are called ridges and the space between ridges are valleys. Fingerprint features (associated with some matching algorithm): ridge pattern - global pattern matching; minutiae (ridge ending and ridge bifurcation) - minutiae matching; - attributes: type, (x,y)- location, orientation 1 and 2 are endings; 3 is bifurcation

January 21, Feature Types pore location - the finest level of detail Required resolution: 1000 dpi core and delta are used for classification or as landmarks; - core is a center of pattern - delta is a point where three patterns deviate;

January 21, Block Diagram

January 21, Image processing Goal: to obtain the best quality leading to the best match result. Steps: - image noise reduction and enhancement, - segmentation, - singularity detection, - manutiae detection, and - matching. Image specifications: - 8-bit gray scale (256 levels); dpi resolution; - (1-by-1) inch size.

January 21, Image Enhancement Noise in the fingerprint image is due to: dry or wet skin, dirt, cut, worn, noise of the capture device. Two image enhancement operations: (i) the adaptive matched filter (enhances ridges oriented in the same direction as those Iin the same locality) ; (ii) adaptive thresholding (binarization: im2bw; graythresh). Estimation of orientation field (gradient method, slit-sums, etc.). Local adaptive thresholding can be used (images with different contrast). Binarized Image Orientation Field

January 21, Image Enhancement Thinning reduces ridge width to a single pixel (Matlab: bwmorph) Preserves connectivity and minimizes the number of artifacts, e.g. erroneous bifurcations. Conclusions: Image processing is time consuming. However, the results of all subsequent operations depend on the quality of image as captured and processed at this stage. Thinned Image

January 21, Other image enhancement methods Image can be divided into windows. Local ridge orientation is found for each window. Spatial or frequency domain processing. D. Maio and D. Maltoni proposed an algorithm that traces ridges and detect minutiae using grayscale image. Multi-resolution approach (multiple window sizes).

January 21, Feature Extraction Singularity and Core Detection: - Poincare index - local histogram method - irregularity operator - multi-resolution approach

January 21, Feature Extraction Endings have one black pixel in 8-neighborhood. Bifurcations have more than 2 black pixels in 8-neighborhood. Noise and previous processing steps produce extraneous minutiae. They can be reduced by a thresholding method. Example: - bifurcation with short branch is a spur; - two endings on a short line is line due to noise; - two endings closely opposing is a broken ridge; - endings at the boundary is due to projection;

January 21, Feature Extraction Each minutia is described by: - minutia type, - (x,y)-location, - minutia direction. Minutia template - minutia with all its attributes. Number of minutiae: from 10 to 100. Type 1 bit Location (each x and y) 9 bits Direction 8 bits Then 100 features require 2700 bits.

January 21, Matching Method 1: - Pick a minutia in one of templates. - Compare a graph formed by its neighborhood against all possible neighborhoods in the second template. (distance between minutiae and their orientations) Use a distance measure to calculate similarity. Result is a match score. Method 2: Align fingerprints using landmarks (core and delta). Core and delta can be found using Poincare index or using estimated orientation flow.

January 21, Matching Method 3: Sort minutiae in some order. Then compare ordered vectors. Method 4: Use other features to describe minutiae (e.g. length and curvature of ridge). Method 5: Matching on the basis of overall ridge pattern (correlation, global matching, image multiplication). Translate one image over another and perform multiplication at each pixel. Find the sum. Sum is the highest when images match. Method 6: Perform correlation matching in frequency domain. Perform 2-D FFT; multiply two transformed images; sum multiplied values. Correlation matching is less tolerable to noise and non-linear transformation. Problems: translational, rotational freedom (depend on landmarks).

January 21, Evaluation Measure of performance? In stochastic estimation and detection, a typical measure is the average probability of error or, for a binary case, ROC curve. There is no good stochastic model. Outcomes are: match or no match. Given a large database of labeled templates. Test the system. Count the number of erroneous decisions.

January 21, Evaluation Fingerprint images are very noisy.

January 21, Evaluation Two fingerprint from two different individuals may produce a high Matching Score (an error); Two fingerprints from the same individual may produce a low Matching Score (an error)

January 21, Evaluation There are two types of error: FAR = ratio of number of instances of pairs of different fingerprints found to (erroneously) match to total number of match attempts. FRR = ratio of number of instances of pairs of same fingerprint are found not to match to total number of match attempts.

January 21, Evaluation Receiver Operating Curve (ROC)

January 21, Image Capture Devices Analog-to-Digital converter Responsible for communicating with external devices Reading device protection/encription (secure identification system) discard fake fingerprints Secure identification system requirements: Additional Issues: storage for large AFIS; compression methods.

January 21, Fingerprint Images: Resolution Number of dots or pixels per inch. Minimum resolution for FBI- compliant scanners Minimum resolution for extracting minutiae (correlation techniques)

January 21, Image Capture Devices Optical: based on frastrated total internal reflection (FTIR) Size: 6 x 3 x 6 in. in 1970s 3 x 1 x 1 in. mid-1990s Cost drop: $ $100 Solid-state sensors: - capacitive, - pressure sensitive, - temperature sensitive Size: 1 x 1 in. (small) Resolution: 500 dpi Ultrasonic scanning: high quality images

January 21, Image Capture Devices Fingerprint sensors can be embedded in a variety of devices for user recognition purposes.

January 21, Image Capture Devices a) a live-scan FTIR-based optical scanner; b) a live-scan capacitive scanner; c) a live-scan piezoelectric scanner; d) a live-scan thermal scanner; e) an off-line inked impression; f) a latent fingerprint

January 21, Available Databases 1. NIST special databases 2. Fingerprint Verification Competition (FVC2000, FVC2002) 3. FBI database (>200 million fingerprints) 4. East Shore Technologies

January 21, References 1. D. Maltoni, et al., Handbook of Fingerprint Recognition, Springer, New York, A. Jain, et al., Biometrics: Personal Identification in Networked Society, Ch. 2, pp , Kluwer Acad. Pub., “An Identity Authentication System Using Fingerprints,” Proceedings of the IEEE, vol. 85, no. 9, 1997, pp L. Hong, Y. Wan, and A. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation,” IEEE Tans. on PAMI, vol. 20, no. 8, 1998, pp K. Karu and A.K. Jain, “Fingerprint Classification,” Pattern Recognition, Vol. 29, No. 3, pp , A.K. Jain, L. Hong and R. Bolle, “On-line Fingerprint Verification,” IEEE Trans. on PAMI, Vol. 19, No. 4, pp , 1997.