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Presentation on theme: "BIOMETRICS."— Presentation transcript:


2 CONTENTS Introduction History General system Finger Print Recognition
Hand Geometry Iris Speaker Verification Performance & Application Conclusion

3 AIMS AT….. Forget passwords ... Forget pin numbers ...
Forget all your security concerns ... WHY USE IT? Tokens, such as smart cards, magnetic stripe cardsand physical keys can be lost, stolen, or duplicated. Passwords can be forgotten, shared, or unintentionally observed by a third party. Forgotten passwords and lost smart cards are a nuisance for users and waste the expensive time of system administrators. Can potentially prevent unauthorized access to or fraudulent use of ATMs, cellular phones, smart cards, desktop PCs, workstations, and computer networks

4 WHAT IS BIOMETRICS? Automated method -automatic identification of of person using human body as a password. Pattern recognition system -computer systems can record and recognize the patterns, hand shapes, ear lobe contours, and a host of other physical characteristics . Specific physiological or behavioral characteristics – Physiological characteristics -> visible parts of the human body which include fingerprint, retina, palm geometry, iris, facial structure, etc. Behavioral characteristics ->what a person does which include voice prints, signatures, typing patterns, key-stroke pattern, gait which is affected by mood, stress, fatigue, and how long ago you woke up


6 HISTORY Fingerprints were first used to identify individuals in ancient China The Henry Classification system, named after Edward Henry who developed and first implemented the system in 1897 in India, was the first method of classification for fingerprint identification based on physiological characteristics First commercial use of biometrics was in the 1960's and 1970's. In the late 1960's FBI developed a system for automatically checking /comparing and verifying fingerprints. In early 1970's FBI installed automatic fingerprinting scanning system. In late 1970's Idnetiymat installed the first biometrics physical access control systems in top secret US Government sites. The system was based on Hand Geometry. Late 1970s development of voice recognition systems. 1980's Biometrics systems using Iris scan and that with face recognition system developed.

7 BIOMETRIC FORMS Fingerprints Hand veins Voiceprints Retina Scan
Facial features Signature Writing patterns Voice Recognition Iris patterns Facial thermograph Hand geometry Odor Keystrokes Gait DNA Ear canal

8 CHARACTERISTICS Universality: Every person should have the characteristic. People who are mute or without a fingerprint will need to be accommodated in some way. Uniqueness: Generally, no two people have identical characteristics. However, identical twins are hard to distinguish. Permanence: The characteristics should not vary with time. A person's face, for example, may change with age. Collectibility: The characteristics must be easily collectible and measurable. Performance: The method must deliver accurate results under varied environmental circumstances. Acceptability: The general public must accept the sample collection routines. Nonintrusive methods are more acceptable. Circumvention: The technology should be difficult to deceive.



11 FINGERPRINT Oldest form of Biometrics Highly Reliable
Uses distinctive features of fingers Finger-scan biometrics is based on the distinctive characteristics of the human fingerprint A fingerprint image is read from a capture device Features are extracted from the image A template is created for comparison

Global features- you can see with the naked eye. Basic Ridge Patterns Pattern Area Core Point Delta Line types Ridge Count Local features- Minutia Points are the tiny, unique characteristics of fingerprint ridges that are used for positive identification. It is possible for two or more individuals to have identical global features but still have different and unique fingerprints because they have local features - minutia points - that are different from those of others.


14 STAGES Fingerprint Scanning Fingerprint Matching
Fingerprint Classification

15 FINGERPRINT SCANNING It’s the acquisition and recognition of a person’s fingerprint characteristics for identification purposes optical method ->which starts with a visual image of a finger. semiconductor-generated-> electric field to image a finger FINGERPRINT MATCHING Minutiae-based ->techniques first find minutiae points and then map their relative placement on the finger. Correlation-based ->techniques require the precise location of a registration point and are affected by image translation and rotation.

16 Fingerprint Classification
It 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 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 An algorithm to classify fingerprints into five classes, namely, whorl, right loop, left loop, arch, and tented arch. An automatic recognition requires that the input fingerprint be matched with a large number of fingerprints in a database . Input fingerprint is required to be matched only with a subset of the fingerprints in the database


18 IMAGE CAPTURE Minutia matching -microscopic approach that analyzes the features of the fingerprint, such as the location and direction of the ridges, for matching Global pattern matching- macroscopic approach where the flow of the ridges is compared at all locations between a pair of fingerprint images


20 IMAGE CAPTURE Image Verification
Optical Scanner - captures a fingerprint image using a light source refracted through a prism Thermal Scanner - very small sensor that produces a larger image of the finger and is contrast-independent Capacitive Scanner - uses light to illuminate a finger placed on a glass surface and records the reflection of this light with a solid-state camera Image Processing image features are detected and enhanced for verification against the stored minutia file. Image enhancement is used to reduce any distortion of the fingerprint caused by dirt, cuts, scars, sweat and dry skin. Image Verification At the verification stage, the image of the fingerprint is compared against the authorized user’s minutia file to determine a match and grant access to the individual





25 ISSUES Privacy - Comparison and storage of unique biological traits makes some individuals feel that their privacy is being invaded. False Rejection- False rejection occurs when a registered user does not gain access to the system. False Acceptance-False acceptance is when an unauthorized user gains access to a biometrically protected system. Accuracy- instances where a fingerprint may become distorted and authorization will not be granted to the user.

26 Hand Geometry This approach uses the geometric shape of the hand for authenticating a user's identity.Individual hand features are not descriptive enough for identification. However, it is possible to devise a method by combining various individual features to attain robust verification. Hand geometry systems use an optical camera to capture two orthogonal twodimensional images of the palm and sides of the hand, offering a balance of reliability and relative ease of use. They typically collect more than 90 dimensional measurements, including finger width, height, and length; distances between joints; and knuckle shapes. These systems rely on geometry and do not read fingerprints. Hand geometry readers can function in extreme temperatures and are not impacted by dirty hands (as fingerprint sensors can be). Hand geometry devices are able to withstand wide changes in temperature and function in a dusty environment.

27 Hand Geometry vs Fingerprints
Unlike fingerprints, the human hand isn't unique. One can use finger length, thickness, and curvature for the purposes of verification but not for identification. For some kinds of access control like immigration and border control, invasive biometrics (e.g., fingerprints) may not be desirable as they infringe on privacy. In such situations it is desirable to have a biometric system that is sufficient for verification. As hand geometry is not distinctive, it is the ideal choice. Hand geometry data is easier to collect. With fingerprint collection good frictional skin is required by imaging systems, and with retina-based recognition systems, special lighting is necessary. Additionally, hand geometry can be easily combined with other biometrics, namely fingerprint. One can envision a system where fingerprints are used for (infrequent) identification and hand geometry is used for (frequent) verification.


29 IRIS RECOGNITION Pattern recognition technique - Iris recognition combines computer vision, pattern recognition, statistics, and the human-machine interface. Identification by mathematical analysis of the random patterns. Based upon the qualities of the Iris. Each person has a distinct pattern of filaments, pits and striations in the colored rings surrounding the pupil of each eye.

30 Iris is a protected internal organ whose random texture is stable throughout life

31 IRIS PROPERTIES High degree of Randomness
No two Iris are alike – No two humans have same iris even one just have different iris in both eyes. Stable in a persons life Doesn't vary with changes

32 IRIS SCAN Camera at close proximity Captures photograph
Uses Infra red light to illuminate High resolution photograph


34 IRIS CODE Localization of inner and outer boundaries-
We detect the inner boundary between the pupil and the iris by means of threshold. The outer boundary of the iris is more difficult to detect because of the low contrast between the two sides of the boundary. We detect the outer boundary by maximizing changes of the perimeter- normalized sum of gray level values along the circle. Pattern of 512 bytes Complete and Compact description More complete than features of DNA

35 IRIS SYTEM Uniform distribution Stored templates Reject Pre processing
Feature-extraction Identification Verification Accept Iris scan image capture Iris localization Iris code comparison

36 IRIS RECOGNITION Database of millions of records
Iris code generated is compared Searching algorithm based on Properties of Iris Order of a few seconds


38 Voice & Signature recognition
Does not measure the visual features In voice recognition sound vibrations of a person is measured and compared to an existing sample. Dynamic signature verification technology used. Analyzing the shape, speed, stroke, and pen pressure and timing information during the act of signing.

39 Face & Palm recognition
In order to recognize a person, one commonly looks at face, which distinguish one person from another. In palm recognition a 3-dimensional image of the hand is collected. The feature vectors are extracted and compared with the database feature vectors.

FAR The FAR is the chance that someone other than you is granted access to your account. Low false acceptance rate is most important when security is the priority

FRR The FRR is the probability that are not authenticated to access your account. A low FRR is required when convenience is the important factor

FAR - As low as 1 in 1,000,00 FRR –around 4%

43 IRIS PERFORMANCE FAR - As low as 1 in 1,000,000 FRR –around 2%

44 APPLICATIONS Criminal identification Prison security ATM
Aviation security Border crossing controls Database access

Scent Ear Shape Finger nail bed Facial 3D




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