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ITI Distinguished Lecture: Kevin Bowyer: "Next-Generation Iris Biometrics"

Event Type
Lecture
Sponsor
Information Trust Institute
Location
B02 Coordinated Science Laboratory
Date
Dec 1, 2008   10:00 am  
Speaker
Kevin W. Bowyer, University of Notre Dame
Originating Calendar
Information Trust Institute (ITI) archival calendar

ABSTRACT:

This talk begins with an overview of the dominant approach to iris biometrics. This approaches uses a binary "iris code" that represents the texture pattern of a given iris. The existence of "inconsistent bits" in the iris code is documented and explained, with masking of inconsistent bits suggested as a way of improving accuracy. The effects of pupil dilation on iris code matching are documented, showing the importance of incorporating knowledge of the degree of pupil dilation. Lastly, texture distortions that can occur due to wearing contact lenses are illustrated, and a means of detecting and compensating for such distortions is proposed.

No prior knowledge of iris biometrics should be needed in order to follow the essentials of this talk.

Reception to follow in 301 Coordinated Science Laboratory.

BIOGRAPHY:

Kevin Bowyer currently serves as Schubmehl-Prein Professor and Chair of the Department of Computer Science and Engineering at the University of Notre Dame. His recent research activities focus on problems in biometrics and in data mining. Particular contributions in biometrics include algorithms for improved accuracy in iris biometrics, face recognition using three-dimensional shape, 2D and 3D ear biometrics, advances in multi-modal biometrics, and support of the government's Face Recognition Grand Challenge, Iris Challenge Evaluation, Face Recognition Vendor Test 2006 and Multiple Biometric Grand Challenge programs. His paper "Face Recognition Technology: Security Versus Privacy," published in IEEE Technology and Society, was recognized with an "Award of Excellence" from the Society for Technical Communication in 2005. His data mining research has been supported by Sandia National Laboratories. This work focuses on classifier ensemble techniques for problems that exhibit "extreme" characteristics, such as a high imbalance between classes, unusually large size of training data, and noise in the class labels of the training data. Professor Bowyer is an IEEE Fellow and the founding General Chair of the IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS).

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