DIMENSIONALITY REDUCTION AND FEATURE-EXTRACTION APPLICATIONS IN IDENTIFYING COMPUTER USERS

Algorithms for dimensionality reduction and feature extraction and their applications as effective pattern recognizers in identifying computer users are presented. Fisher's Linear discriminant technique was used for the reduction of dimensionality of the patterns. A new approach for the extraction of physical features from pattern vectors is developed. This approach relies on shuffling two pattern vectors. The shuffling approach is competitive to the use of Fisher's technique in terms of speed and results. An on-line identification system was developed. The system was tested over a period of five weeks, used by ten participants, and gave an error of 1.17% of unable to decide error. The applications of these algorithms in identifying computer users could lead to better results in securing access to computer systems.