Overview of Biometric Projects

The common biometrics include face, iris, fingerprint, voice print, etc. This semester's biometric projects include the less common biometrics of mouse movements, stylometry, and keystroke patterns. We have chosen 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 accuracy.

This semester we will continue some of the projects on the mouse movement, stylometry, and keystroke biometrics. We will also continue the project on the generic biometric authentication system. In the earlier 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. Beginning last semester we focused on the authentication problem, which is usually considered the more important one and the one for which comparable evaluation statistics can be obtained. This semester's biometric projects will again focus on the front-end system components, data gathering and feature extraction. The feature data will be provided to the authentication team for back-end classification processing. The feature vector files are to be in the specified text readable format.