BIO-MEDICAL INSTRUMENTATION

ABSTRACT
Biometric security is a topic of rapidly growing importance, especially as it applies to user authentication and key generation. In this paper, we describe our initial steps towards developing evaluation methodologies for behavioral biometrics that take into account threat models, which have largely been ignored. We argue that the pervasive assumption that forgers are minimally motivated (or, even worse, naïve), or that attacks can only be mounted through manual effort, is too optimistic and even dangerous. Biometrics is best defined as measurable and/or behavioral characteristics that can be employed to verify the identity of a person.
They include fingerprints, retinal and iris scanning, hand-writing and geometry, voiceprints, facial recognition, DNA codes, and other techniques and features Additionally, to overcome current labor-intensive hurdles in performing more accurate assessments of system security, a generative attack model based on concatenate synthesis can be provided by a rapid indication of the security afforded by the system. We show that our generative attacks match or exceed the effectiveness of forgeries rendered by the skilled humans.
Biometrics is seen by many as a solution to a lot of the user identification and security problems in today‘s networks. Password abuse and misuse, intentional and inadvertent is a gaping hole in network security. This results mainly from human error, carelessness and in some cases maliciousness. Biometrics removes human error from the security equation.





INTRODUCTION TO BIOMETRICS
When you want to uniquely prove your identity, you rely on
Something you know: PIN, password.
Something you have: key, token, card...
Does not insure that you are the real owner.
A small part of the security system,
Aim is to replace a password or a key
HISTORY
Pre-historic
Picture writing of a hand with ridge patterns was discovered in Nova Scotia. In ancient Babylon, fingerprints were used on clay tablets for business transactions.
1686Malpighi
In 1686, Marcello Malpighi, a professor of anatomy at the University of Bologna, noted in his treatise; ridges, spirals and loops in fingerprints. He made no mention of their value as a tool for individual identification. A layer of skin was named after him; "Malpighi" layer, which is approximately 1.8mm thick.
2004
The FBI's Integrated AFIS (IAFIS) in Clarksburg, WVhas more than 46 million individual computerized fingerprint records for known criminals.


WHAT BIOMETRIC SYSTEM?
Choosing a biometric system
Security level: An idea of what security level
Universal for everyone: It ALWAYS exist a (low) percentage of persons unable to use a kind of biometrics: skin disease, blind people… excepted for DNA! What is your backup solution if the user cannot enroll?
Enrollment / authentication speed: what is the acceptable time to enroll? To recognize? Is the enrollment assisted?
Ergonomics, convenient, accepted way of giving sample:
SECURITY BIOMETRICS
Security
This is not because you are using a biometric system that it is secure: biometrics are only a small brick in a secure system, and you must be cautious when speaking of security. The security of a fingerprint system may be divided into two main areas:
Electronic security: is it an authorized fingerprint system we have at the other end of the wire?
Live ness security: is this finger alive, fake, or dead?
TECHNIQUES
Several ergonomics exist to acquire a fingerprint:
Rolled fingerprints: the user rolls his/her finger in order to get the maximum fingerprint area.
Static sensing: the user just puts his/her finger on the sensor.
Sweeping reading: the user sweeps his/her finger on the sensor.
Sweep (or swipe) reading
Common problems of sweep reading are:
This is not a natural way of acquisition, user has to learn how to use it.
The reader is always clean: each swipe cleans the sensor.
No latent print on the sensor.
No feeling of "leaving" his/her fingerprint: the swipe is short.
The very first commercial sweep sensor was the FingerChip using the thermal effect.



Fingerprint:
Algorithms
Comparison methods
If manual comparison by a fingerprint expert is always done to say if two fingerprint images are coming from the same finger in critical cases, automated methods are widely used now. Many different algorithm types exist:
Some algorithms count the number of ridges between particular points, generally the minutiae, instead of the distances computed from the position. Very often, algorithms are using a combination of all theses techniques
BIOMETRICS:TYPES
FINGER PRINTS
Finger skin is made of friction ridges, with pores (sweat glands). Friction ridges are created during foetal live and only the general shape is genetically defined. Friction ridges remain the same all life long, only growing up to adult size.
Some skin diseases such as psoriasis cause problems for proper fingerprint recognition. Scars produce some unusual patterns that are easily recognizable.

FOOT PRINT
In some countries, a footprint of each baby is collected for identification purpose.
Facial geometry:
It uses geometrical characteristics of the face. May use several cameras to get better accuracy (2D, 3D...)
Skin pattern recognition (Visual Skin Print)
Facial thermogram: uses an infrared camera to map the face temperatures
Smile: recognition of the wrinkle changes when smiling
HANDS
A camera captures an image of the hand, with the help of a mirror to get also the edge. The silhouette of the hand is extracted, and some geometrical characteristics stored. Recognition Systems' Hand Reader Technology verifies identities by the size and shape of the hand.
Advantages
Security level:
Cost adapted
Universal for everyone. Is it a problem if some categories of persons are always rejected?
Sensor size: a portable system, such as a cellular phone, will require a very small sensor.
Signature token: what about the storage of the signature cost, reliability, security
Privacy: is it an issue for your system.
Popular confidence: population will immediately trust a fingerprint system while a
System based on gait recognition will not. Is it important in your application?
Gait recognition is particularly studied as it may enable identification at distance.
No two retinas are the same, even in identical twins
The canal of the ear, which returns a specific echo.
Disadvantages
The system cannot expect a 100% match.
Some skin diseases such as psoriasis cause problems for proper fingerprint recognition.
Scars produce some unusual patterns that are easily recognizable.
For the moment, the laughter-recognition software is rather crude and cannot accurately distinguish between different people.
In this case, speed & pressure are not available and the recognition accuracy decreases. It is not yet fully automated (and fast).
Iris, retina: think about blind people. Aniridia (absence of iris) is a phenomenon found in a proportion of 1.8 out of 100.000 births, which affects both eyes for genetic reasons (US National Eye Institute).
Fingerprints: even without amputation, there are some people, who have some skin diseases, which prevent normal formation of fingerprints, and we even know the case of one family, which have no fingerprints (for genetic reason, probably).
Voice recognition: some people are not able to speak.
Keystroke dynamics: I'm pretty sure that you know some people just able to use two fingers to type...


Conclusion
A good biometric system designer must always include a special procedure for those unable to use the chosen biometrics. This causes security problems -exceptions always cause problems-. For instance, if you install a brand new fingerprint system, but you kept the 4-numbers PIN in parallel for exceptions, well, your system will have the same security level than the 4-numbers PIN system, no more... but it will be more convenient!
<span style="font-weight:bold;">Reference:
1. A.K. Jain, Y. Chen and M. Demirkus, " Pores and Ridges: High Resolution Fingerprint Matching Using Level 3 Features", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006.
2. Y. Chen, M. Demirkus and A.K. Jain, " Pores and Ridges: Fingerprint Matching Using Level 3 Features", Proc. of International Conference on Pattern Recognition (ICPR), Vol. 4, pp. 477-480, Hong Kong, August, 2006