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).
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 files must be in the specified text-readable Feature Data Format.