U of HCOSC 6397 – Lecture 2 #1 U of HCOSC 6397 Lecture 2: Introduction to Biometrics (II) Prof. Ioannis Pavlidis

U of HCOSC 6397 – Lecture 2 #2 DNA DNA (DeoxyriboNucleic Acid) is the 1D ultimate unique code for one’s individuality. Identification for forensic applications only. Three factors limit the utility of this biometric for other applications –Contamination and sensitivity –Automatic real-time identification issues –Privacy issues

U of HCOSC 6397 – Lecture 2 #3 Signature and Acoustic Emissions The way a person signs her name is known to be a characteristic of that individual. A related technology is authentication of an identity based on the characteristics of the acoustic emissions emitted during a signature scribble. Pros –Acceptable practice in many transactions Cons –They require contact and effort –They evolve over time and are influenced by physical and emotional conditions of the signatories. –Professional forgers can reproduce signatures to fool the unskilled eye.

U of HCOSC 6397 – Lecture 2 #4 Odor It is known that each object exudes an odor that is characteristic of its chemical composition and could be used for distinguishing various objects. Methodology –A whiff of air surrounding an object is blown over an array of chemical sensors, each sensitive to a certain group of aromatic compounds. –The feature vector consists of the signature comprising of the normalized measurements from each sensor. –After each act of sensing, the sensors need to be initialized by a flux of clean air.

U of HCOSC 6397 – Lecture 2 #5 Retinal Scan The retinal vasculature is rich in structure and is supposed to be characteristic of each individual and each eye. Pros –It is supposed to be the most secure biometric since it is not easy to change or replicate the retinal vasculature. Cons –The image acquisition involves cooperation of the subject, entails contact with the eyepiece, and requires a conscious effort on the part of the user.

U of HCOSC 6397 – Lecture 2 #6 Hand and Finger Geometry Some features related to a human hand, e.g., length of fingers, are relatively invariant and peculiar (although, not unique) to each individual. Finger geometry is a variant of hand geometry and is a relatively new technology, which relies only on geometrical invariants of fingers (index and middle). Pros –The representational requirements of the hand are very small (9 bytes). Cons –Suitable for verification only. –It requires cooperation from the subject. The registration of the palm is accomplished by requiring the subject’s fingers to be aligned with a system of pegs on the panel, which is not convenient for subjects suffering from arthritis.

U of HCOSC 6397 – Lecture 2 #7 Biometrics Technologies: A Comparison In the context of biometrics-based identification (authentication) systems, an application is characterized by the following properties: –Does the application need identification or authentication? –Is it attended (semi-automatic) or unattended (completely automatic)? –Are the users habituated (or willing to be habituated) in the given biometric? –Is the application overt or covert? –Are the subjects cooperative or non-cooperative? –What are the storage requirement constraints? –How stringent are the performance requirement constraints? –What types of biometrics are acceptable to the users?

U of HCOSC 6397 – Lecture 2 #8 Research Issues - Design Design of a biometrics-based identification system could essentially be reduced to the design of a pattern recognition system. The following design issues need to be addressed: –How to acquire the input data/measurements (biometrics)? –What internal representation (features) of the input data is invariant and amenable for an automatic feature extraction process? –Given the input data, how to extract the internal representation from it? –Given two input samples in the selected internal representation, how to define a matching metric that translates the intuition of “similarity” among the patterns? –How to implement the matching metric? –Organization of a number of (representations) input samples into a database. –Effective methods of searching a given input sample representation in the database.

U of HCOSC 6397 – Lecture 2 #9 Acquisition Issues: –Quality assessment; automatically assessing the suitability of the input data for automatic processing. –Segmentation; separation of the input data into foreground (object of interest) and background (irrelevant information). A lot of research is needed to systematize: –Rigorous and realistic models of the input measurements. –Metrics for assessment of quality of a measurement.