Presentation Transcript

1. BIOMETRIC AUTHENTICATION

   SUBMITTED BY:-- SAYANI MONDAL ROLL NO– 10IT61K01 INFORMATION AND COMMUNICATION TECHNOLOGY
2. WHAT ARE BIOMETRICS ? Biometrics are automated methods of identifying a person or verifying the identity of a person based on a physiological or behavioral characteristic. Fig:- Basic structure of a biometric system
3. A biometric system can be divided into two stages: the verification module the identification module Before that ENROLLMENT should be done , in which a sample of the biometric feature is captured, processed by a computer, and stored for later comparison. FIG:-ENROLLMENT User interface (name ,pin, fingerprint) Quality checker Feature extractor System database
4. Verification :-In this mode, biometric system authenticates a person’s claimed identity from their previously enrolled pattern. This is also known as one to one matching. Identification:-In this mode, the biometric system identifies a person from the entire enrolled information by searching a database for a match. This is also known as one to many matching. CLAIMED IDENTITY 1 TEMPLATE User interface (name ,pin, fingerprint) SYSTEM DB FEATURE EXTRACTER MATCHER TRUE / FALSE N TEMPLATE User interface (name ,pin, fingerprint) SYSTEM DB FEATURE EXTRACTOR MATCHER USERS IDENTITY
5. CLASSIFICATION OF BIOMETRICS
6. Fingerprint Recognition fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Speech Recognition speaker recognition uses the acoustic features of speech that have been found to differ between individuals. Face Recognition Identification of a person by their facial image can be done by capturing an image of the face in the visible spectrum . Signature Signature is used to verify User Authentication
7. UNDERSTANDING THE HUMAN IRIS The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. The function of the iris is to control the amount of light entering through the pupil. Since iris features are distinct from one person to another, these are considered in iris-based recognition process.
8. IRIS RECOGNITION Fig:- enrollment process Fig:- matching process IRIS IMAGE AQUISITION IRIS LOCALIZATION IRIS NORMALIZATION FEATURE ENCODING DB IRIS IMAGE AQUISITION IRIS NORMALIZATION IRIS LOCALIZATION Feature encoding Match /reject Matching DB
9. STAGES INVOLVED IN IRIS DETECTION It includes Three Main Stages:- Image Acquisition and Segmentation – locating the iris region in an eye image Image Normalization – creating dimensionally consistent representation of the iris region Feature Coding and Matching– creating a template containing only the most differentiating feature of the iris.
10. IMAGE ACQUISITION AND SEGMENTATION The Daugman image-acquisition System:-- One of the major challenges of automated iris recognition is to capture a high-quality image of the iris. Obtained images with sufficient resolution and sharpness. Good contrast in the interior iris pattern with proper illumination.
11. SEGMENTATION Daugman’sIntegro-differential Operator :-- It is used to locate circular iris and pupil boundary regions and also the arc of the upper and lower eye lids and this is done by Where I(x,y) is the eye image as the raw input , r is the increasing radius and center coordinates (x0,y0), * denotes convolution ,Gσ(r) is a Gaussian smoothing function, (x0,y0,r) define the path of the contour integration
12. Isolation of the iris from the rest of the image. The white graphical overlays signify detected iris boundaries resulting from the segmentation process.
13. NORMALIZATION Daugman’s Rubber Sheet Model The points between the inner and outer boundary contours are interpolated linearly by a homogeneous rubber sheet model, which automatically changes the iris pattern deformations caused by pupillary dilation or constriction.
14. The homogenous rubber sheet model assign to each point in the iris a pair of dimensionless real coordinates (r, θ) where r lies in the unit interval (0,1) & θ is the angle (0,2π). The remapping or normalization of the iris image I(x,y) from raw coordinates (x,y) to non concentric coordinate system (r, θ). Where I(x,y) are original iris region Cartesian coordinates (xp(θ),yp(θ)) are coordinates of pupil,(xs (θ),ys (θ)) are the coordinates of iris boundary along the θ direction.
15. FEATURE ENCODING The iris is encoded to a unique set of 2048 bits which serve as the fundamental identification of that persons particular iris. The iris pattern is then demodulated to extract the phase information using 2D Gabor wavelets:-
16. where h{Re;Im} can be regarded as a complex-valued bit whose real and imaginary parts are either 1 or 0 depending on the sign of the 2D integral; is the raw iris image in a dimensionless polar coordinate system. α and β are the multi-scale 2D wavelet size parameters. Only phase information is used for recognizing irises because amplitude information is not very discriminating, and it depends upon extraneous factors such as imaging contrast, illumination, and camera gain.
17. MATCHING For matching , a test of statistical independence is required which helps to compare the phase codes for 2 different eyes. Exclusive OR operator (XOR) is applied to 2048 bit phase vectors that encode any 2 iris templates, AND ed by both of their corresponding mask bit vectors to prevent non iris artifacts from influencing iris comparison. The XOR operator detects disagreement between any corresponding pair of bits, while AND operator ensures that the compared bits are not corrupted by eyelashes . The norms(|| ||) of resultant bit vector and the AND ed mask vector are computed to determine a fractional Hamming distance.
18. Hamming distance is the measure of dissimilarity between any 2 irises. HD= ||(code A XOR code B) AND (mask A AND mask B)|| ||( mask A AND mask B)|| Where {code A, code B} are phase code vectors bit And {mask A ,mask B} are mask bit vectors. We can see that the numerator will be the number of differences between the mutually non-bad bits of code A and code B and that the denominator will be the number of mutually non-bad bits. If HD result is 0 it is a perfect match.
19. Biometric System Performance The following are used as performance metrics for biometric systems: false accept rate or false match rate (FAR or FMR) false reject rate or false non-match rate (FRR or FNMR) equal error rate or crossover error rate (EER or CER) failure to enroll rate (FTE or FER) failure to capture rate (FTC)
20. ADVANTAGES It is an internal organ that is well protected against damage by a highly transparent and sensitive membrane. This feature makes it advantageous from finger print. Flat , geometrical configuration controlled by 2 complementary muscles control the diameter of the pupil makes the iris shape more predictable . An iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away. Encoding and decision-making are tractable . Genetic independence no two eyes are the same.
21. DISADVANTAGES The accuracy of iris scanners can be affected by changes in lightning. Obscured by eyelashes, lenses, reflections. Deforms non-elastically as pupil changes size. Iris scanners are significantly more expensive than some other form of biometrics.
22. APPLICATIONS Used in ATM ’s for more secure transaction. Used in airports for security purposes. Computer login: The iris as a living password Credit-card authentication Secure financial transaction (e-commerce, banking). “Biometric—key Cryptography “for encrypting/decrypting messages
23. CONCLUSION There are many mature biometric systems available now. Proper design and implementation of the biometric system can indeed increase the overall security. There are numerous conditions that must be taken in account when designing a secure biometric system. First, it is necessary to realize that biometrics are not secrets. This impliesthat care should be taken and it is not secure to generate any cryptographic keys from them. Second, it is necessary to trust the input device and make the communication link secure. Third, the input device needs to be verified .
24. REFERENCES J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148–1161, 1993. J. G. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and System for Video Technology”, vol. 14, no. 1, pp. 21-30, 2004. 3. Amir Azizi and Hamid Reza Pourreza,, “Efficient IRIS Recognition Through Improvement of Feature Extraction and subset Selection”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 2, No.1, June 2009. www.wikipedia.com 5. ParvathiAmbalakat,” Security of Biometric Authentication Systems”. John Daugman, The Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK,” The importance of being random: statistical principles of iris recognition”. 7. SomnathDey and DebasisSamanta,” Improved Feature Processing for Iris Biometric Authentication System”, International Journal of Electrical and Electronics Engineering 4:2 2010