The common biometrics include face, fingerprint, hand geometry, iris, signature, and voice,
see Wikipedia on Biometrics.
Some of the less common biometrics that we have explored are mouse movement, stylometry, and keystroke
patterns. We often explore the less common biometrics because it is easier to perform new and unique research and to publish in these areas. All biometrics have authentication and identification
applications. In authentication (verification) applications a user is either accepted or rejected (binary response, yes you are the person you claim to be or no you are not). In
identification applications a user is identified from within a population of, say, n users (one-of-n response), which is usually a more difficult problem.
Biometric systems are basically pattern recognition systems that typically consists of three components: data collection, feature extraction, and classification. The data collection component
captures the raw data of the object. For face recognition, for example, it might be a photo. Operating on the raw data, the feature extraction component calculates feature measurements, such eye
color, hair color, eye spacing, hair texture, nose size relative to face size, nose shape, ear size relative to face size, ear shape, etc. Operating on the feature measurements, usually referred to
as the feature vector, the classifier decides which class to place the object into. For example, if the system is trying to identify individuals from a population of n people, then there are
n classes; if the system is trying to distinguish males from females, then there are two classes. Usually the difficulty of the problem increases as the number of classes increases. A
pattern recognition system must be trained to become usable, so the data are usually separated into two parts, one for training the system to create decision boundaries in feature space and one for
testing the system to determine its performance (accuracy).
CSIS Biometrics Projects
In the early projects we attacked the identification problem to establish the feasibility of the biometric,
and for ease of implementation we used a simple classification technique called nearest neighbor.
More recently we focused on the authentication problem and developed a generic biometric authentication model.
Authentication is usually considered the more important problem
and the one for which comparable evaluation statistics can often be obtained.
As in the recent biometric projects, we will continue, where possible,
to separate the front-end and back-end system components.
The front-end components consist of data gathering and feature extraction.
The backend-end consists of the classifier which receives the feature vector as input
and provides the classification output.
In order to use our generic model, the feature vector components must be standardized into the range 0-1.
Information relating to some of our CSIS biometric projects
Conferences on security and biometrics
- Yoon, et al., On the Individuality of the Iris Biometric, describes dichotomy model.
- Wikipedia, k-nearest neighbor algorithm.
- Gibbons, et al., Evaluation of Biometric Identification in Open Systems.
- Gibbons, et al., Biometric Identification Infer-ability.
- Cha, Use of Distance Measures in Handwriting Analysis, Dissertation, 2001.