1. A User Verification System: Spring, 2002 Timely Problems, Novel Solutions Project Team Members: William Baker, Arthur Evans, Lisa Jordan, Saurabh Pethe Under the guidance of Professor Sung-Hyuk Cha, PhD

2. Introduction & Motivation Automated user verification allows us to verify quickly and accurately whether a person is who he/she claims to be. Within field of Biometrics  Capitalize on human variability to distinguish each other. Security relevance & economic potential: –Airports, banking industry, government agencies –Consider: 1999-2000 biometric industry revenues jump 60% ($250 to $400 million year-over-year).

3. What makes our verification system unique? 1.) We believe we have a solution to the “many class” problem that large systems face: As user pool gets large, distinguishing power of biometric feature template begins to erode as it becomes more likely to have two users with same or nearly similar sets of features. Cha & Srihari at SUNY, Buffalo look at feature relationships stemming from: 1.) differences or numerical “distances” within samples of same user, versus 2.) differences between samples of different users AND that of the original user. Removes key hurdle to wider commercial adoption of biometrics We can now on application serving large user populations, such as a Federal agency or multinational corporation. 2.) We extract features across multiple modalities: Expand potential sources of human uniqueness to include face, fingerprint, voice, and handwriting modes of measurement.  Greater accuracy achieved.

4. We transform classification of many into dichotomy: “within” vs. “between-sample” feature distances. Accuracy robust, holds even with large user pool.

5. Verification results include false positive & false negative errors. How else can we minimize error?

6. Automated User Verification System: Stages of Implementation 1.Raw data collection 2.Pre-processing of data 3.Extract features, calculate distances 4.Train Artificial Neural Network 5.Test, adjust feature set  Aim to minimize error rates independent of data—i.e., the model holds. 6.User interface in Java; for calculation power we use MATLAB functions.

7. Automated User Verification System: Current Challenges, Future Directions Always in search of good data, lots of it. Testing system and refining feature selection to get best mix  esp. statistically independent features, stable over time. Working on integrating Java-based user interface with Matlab’s neural network pkg. Interested in possible collaboration with others over the summer.