1. Fundamentals of Biometrics for Personal Verification/Identification Chaur-Chin Chen Department of Computer Science Institute of Information Systems and Applications National Tsing Hua University E-mail: cchen@cs.nthu.edu.twcchen@cs.nthu.edu.tw Tel/Fax: (03) 573-1078/ (03) 572-3694

2. Outline What is Biometrics? Motivation by Evidence Iris Image Pattern Analysis Handwriting/Handprinting Verification Personal Signature Verification Hand Geometry Verification Voice (Speech) Pattern Recognition Face Image Recognition Fingerprint Image Verification/Identification Palmprint, Ear shape, Gesture, … Fingerprint Classification and Verification Opportunities and Challenges

3. What and Why is Biometrics? What is Biometrics? Biometrics is the science and technology of interactively measuring and statistically analyzing biological data, in particular, taken from live people. Why Biometrics? (1) The banking industry reports that false acceptance rate (FAR) at ATMs are as high as 30%, which results in financial fraud of US$2.98 billion a year. (2) In U.S., nearly half of all escapees from prisons leave through the front door, posing as someone else. (3) Roughly 4000 immigration inspectors at US ports-of-entry intercepted and denied admission to almost 800,000 people. There is no estimate of those who may have gotton through illegally. (4) Personal verification/identification becomes a more serious job after the WTC attack on September 11, in the year 2001.  The evidence indicates that neither a PIN number nor a password is reliable.

4. Some Biometric Images

5. Iris Image Pattern Analysis The iris is the portion of texture regions surrounding the pupil of an eyeball. The iris image can be sensed by a CCD camera under a regular lighting environment. An ancient French criminologist Berthillon did exploratory work linking iris pattern to prisoner identity. In 1980’s, ophthamologists Leonard Flom and Aran Safar posited that no two irises were alike. In 1994, Professor John Dougman develop algorithms using 2D Gabor filters according to Flom and Safar’s concept to extract iris features for the use in human authentication. IrisCode, the feature vector of an iris, consisting of 512 bytes is recorded and stored in the database for future recognition/matching. It takes less than 2 seconds in a Pentium III machine to compute an IrisCode. Potential applications for iris scanning biometrics are widespread and installations have been undertaken in the financial sectors for CityBank ATMs as well as in some international airport for passenger identification.  http://www.astrontech.pl/html/body_iridian_merged.html http://www.astrontech.pl/html/body_iridian_merged.html

6. Handwriting/Handprinting Verification Personal Signature Verification Handwritings and Signatures are behavioral biometrics rather than anatomical biometrics such as an iris pattern or a fingerprint. People handwrite digits or their names in their own special manners. An ancient Chinese calligrapher Wang, Xizhi (AD 306~365) produced many beautiful writings such that his signature would be paid for in gold. Based on the mechanics of how we write is something very personal and often quite distinctive, biometrics handwriting and/or signature seeks to analyze the dynamics inherent in writing the digits, characters, letters, words, and sentences. The features include how a person presses on the writing surface, how long a person takes to sign his name, how a person struggles to maintain verticality, angularity in letter forms and along the baseline, plus narrow letters. http://www.handwriting.org/main/hwamain.html Biometrics is the science and technology of interactively measuring and statistically analyzing biological data, in particular, taken from live people

7. Hand Geometry Verification Hand geometry systems work by taking a 3D view of the hand in order to determine the geometric shape and metrics around finger length, height, and/or other details. A leading hand geometry device measures and computes around 90 parameters and stores in a record of 9 bytes, providing for flexibility and storage transmission.  http://cse.msu.edu/rgroups http://cse.msu.edu/rgroups

8. Voice (Speech) Pattern Recognition The basis for voice or speech technology was pioneered by Texas Instruments in the 1960’s. The current voice recognition uses a standard microphone to record an individual’s voice and identity its unique characteristics. It attempts to analyze the physiological characteristics that produce speech, and not the sound or pronunciation. A voice identification system requires that a “voice reference template” be constructed so that it can be compared against subsequent voice identification. Voice identification systems incorporate several variables or parameters in the recognition of one’s voice/speech pattern including pitch, dynamics, and waveforms. It is estimated that the revenues from voice/speech identification systems and telephony equipments and services sold in America will increase from US$356 million in 1997 to US$22.6 billion in 2003. Hidden Markov Model and Autoregressive Model Fast Fourier Transform and Wavelet Analysis  http://www.buytel.com http://www.buytel.com

9. Outline For Image Processing A Digital Image Processing System Image Representation and Formats 1. Sensing, Sampling, Quantization 2. Gray level and Color Images 3. Raw, RGB, Tiff, BMP, JPG, GIF, (JP2) Image Transform and Filtering Histogram, Enhancement and Restoration Segmentation, Edge Detection, Thinning Image Data Compression R.C. Gonzalez and R.E. Woods, Digital Image Processing, Prentice-Hall, 2002

10. Digital Image Analysis System A 2D image is nothing but a mapping from a region to a matrix A Digital Image Processing System consists of 1. Acquisition – scanners, digital camera, ultrasound, X-ray, MRI, PMT 2. Storage – HD (40GB+), CD (700MB), DVD (4.7GB), HD-DVD (20GB), Flash memory (256 MB +) 3. Processing Unit – PC, Workstation, PC-cluster 4. Communication – telephone, cable, wireless 5. Display – LCD monitor, laser printer, laser-jet printer

11. Image Processing System

12. Gray Level and Color Images

13. Pixels in a Gray Level Image

14. Gray and Color Image Data 0, 64, 144, 196, 225, 169, 100, 36 (R, G, B) for a color pixel Red – (255, 0, 0) Green – ( 0, 255, 0) Blue – ( 0, 0, 255) Cyan – ( 0,255, 255) Magenta – (255, 0, 255) Yellow – (255, 255, 0) Gray – (128, 128, 128)

15. Image Representation (Gray/Color) A gray level image is usually represented by an M by N matrix whose elements are all integers in {0,1, …, 255} corresponding to brightness scales A color image is usually represented by 3 M x N matrices whose elements are all integers in {0,1, …, 255} corresponding to 3 primary primitives of colors such as Red, Green, Blue

16. Sensing, Sampling, Quantization A 2D digital image is formed by a sensor which maps a region to a matrix Digitization of the spatial coordinates (x,y) in an image function f(x,y) is called Sampling Digitization of the amplitude of an image function f(x,y) is called Quantization

17. Gray Level and Color Images

18. Some Image File Formats Raw – Raw image format uses a 8-bit unsigned character to store a pixel value of 0~255 for a Raster-scanned gray image without compression. An R by C raw image occupies R*C bytes or 8RC bits of storage space TIFF – Tagged Image File Format from Aldus and Microsoft was designed for importing image into desktop publishing programs and quickly became accepted by a variety of software developers as a standard. Its built-in flexibility is both a blessing and a curse, because it can be customized in a variety of ways to fit a programmer’s needs. However, the flexibility of the format resulted in many versions of TIFF, some of which are so different that they are incompatible with each other JPEG – Joint Photographic Experts Group format is the most popular lossy method of compression, and the current standard whose file name ends with “.jpg” which allows Raster-based 8-bit grayscale or 24-bit color images with the compression ratio more than 16:1 and preserves the fidelity of the reconstructed image EPS – Encapsulated PostScript language format from Adulus Systems uses Metafile of 1~24-bit colors with compression JPEG 2000

19. Image and Its Histogram

20. Edge Detection -1 -2 -1 0 0 0  X 1 2 1 -1 0 1 -2 0 2  Y -1 0 1 Large (|X|+|Y|)  Edge

21. Thinning and Contour Tracing Thinning is to find the skeleton of an image which was commonly used for Optical Character Recognition (OCR) and Fingerprint matching Contour tracing is usually used to locate the boundaries of an image which can be used in feature extraction for shape discrimination

22. Image  Edge, Skeleton, Contour

23. Image Data Compression The purpose is to save storage space and to reduce the transmission time of information. Note that it requires 6 mega bits to store a 24-bit color image of size 512 by 512. It takes 6 seconds to download such an image via an ADSL (Asymmetric Digital Subscriber Line) with the rate 1 mega bits per second and more than 12 seconds to upload the same image Note that 1 byte = 8 bits, 3 bytes = 24 bits

24. Lenna Image vs. Compressed Lenna

25. Face Image Recognition Face recognition technology works well with most of the shelf PC cameras, generally requiring 320*240 resolution at 3~5 frames per second. Facial recognition software products range in price from US$50 to over US$1000, making one of the cheaper biometric technologies. Four primary methods used to identify or verify users by means of facial features, including eigenface, PCA, 2D-PCA, LDA, 2D-LDA, wavelet analysis, neural network, and ad hoc methods. Singular Value Decomposition and Pattern Recognition. Fast Fourier Transform and Wavelet Analysis  http://facial-scan.com/facial-scan_technology.htm http://facial-scan.com/facial-scan_technology.htm  http://www-white.media.mit.edu/vismod/demos/facerec http://www-white.media.mit.edu/vismod/demos/facerec

26. A Face Recognition Flowchart

27. Face Database YALE P. N. Belhumer, J. Hespanha, and D. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition, 17(7):711--720, 1997. YALE B Georghiades, A.S. and Belhumeur, P.N. and Kriegman, D.J. From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6):643-660 (2001). ORL Ferdinando Samaria, Andy Harter. Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994 AR A.M. Martinez and R. Benavente. The AR Face Database. CVC Technical Report #24, June 1998

28. Faces From The Same Person

29. Cumulative Distributions of Same Faces

30. Faces from Different Persons

31. Cumulative Distributions of Different Faces

32. Fingerprint Image Verification/Identification Each fingerprint is a map of ridges and valleys in the epidermis layer of the skin. The ridge and valley structures from unique geometric patterns. A minutiae pattern consisting of ridge endings and bifurcations is unique to each fingerprint. Most of the contemporary automated fingerprint identification and verification systems (AFIS) are minutiae pattern matching systems. A modern AFIS is composed of 5 primary modules: (1) Image Enhancement, (2) Image segmentation and Thinning, (3) Minutiae Points Extraction, (4) Core and Delta Localization, and (5) Point Pattern Matching. A fingerprint forum provided 5 sets of small databases for researchers to evaluate their identification/verification software. SecuGen EyeD and Veridicom are two leading companies selling both commercial fingerprint identification/verification systems and sensors with resolution 500dpi. Veridicom FPS110 fingerprint reader sensed a 300*300 fingerprint image in a 2cm by 2cm area. http://www.networkusa.org/fingerprint.shtml http://bias.csr.unibo.it/fvc2000 http://bias.csr.unibo.it/fvc2004 http://www.fpusa.com

33. FINGERPRINTS.DEMON.NL

34. FVC 2004

36. A Paradigm for Fingerprint Matching

37. Fingerprints and Their Histograms

38. Fingerprint Image Processing

40. Thank You Koala Angel wishes you have a wonderful university life I am from Brisbane, Australia and sleep 16 hours each day but you should not April 12, 2007

41. Are They From the Same Person?

42. Are They the Same Person?