IEEE Computer Society Technical Achievement Award Presentation Video.

Plenary Talk at Virginia Tech's CESCA Day - Effects of Contact Lenses on Iris Recognition.

Iris biometrics uses the texture pattern of the iris to create a unique identifier for a person. The iris image to the near right shows moderate pupil dilation and only minor occlusion of the iris region by the lower eyelid. The image to the far right shows a cosmetic contact lens, which obscures nearly all of the natural iris texture. We have published results dealing with "fragile bits" in the iris code; the effects of varying pupil dilation, contact lenses, and template aging; the similarity of iris texture in genetically identical irises; and prediction of "soft biometric" attributes from iris texture.

To the left is an aerial image of a Florida community before Hurricane Dennis in 2005. To the right is an image taken after the storm. Detecting changes based on before- and after-storm images can enable automated and consistent damage assessment. My work in this area is a collaboration with Professor Ahsan Kareem in Civil Engineering. This problem is difficult because the images are not necessarily registered, structures that appear in one image may not appear in the other image, and images taken at different times or under different lighting conditions may have different color properties. A narrated ppt by PhD student Jim Thomas gives an overview of correcting for color change that is due to images being taken under different conditions.

We collected a large biometric dataset from volunteer "identical twins" subjects at the Twins Days Festival in Twinsburg, Ohio. Current commercial face recognition technology has difficulty telling twins apart. However, it appears that human observers can do better by focusing on fine details of the face image. Also, while iris biometrics does not 'see' the similarity in the iris textures of twins, our experiments show that human observers can tell if a pair of iris images come from identical twins or from unrelated persons. We have also explored the use of 3D shape of the face as a means to distinguish twins from each other. We hope that the study of human ability to distinguish twins can lead to improved biometric algorithms.

WNDU news video on biometrics and twins.

Current Research - Biometrics.
My research group has done extensive work in iris recognition, 2D, 3D and infra-red face recognition, face recognition in video, 2D and 3D ear biometrics, multi-modal biometrics and related topics. We have been involved in support of the Human ID Gait Challenge, the Face Recognition Grand Challenge, the Face Recognition Vendor Test 2006, the Iris Challenge Evaluation, and the Multiple Biometric Grand Challenge.

Current Research - Data Mining.
My research in data mining is focused on creating ensembles of classifiers for problems that exhibit "extreme" characteristics such as a high imbalance between classes in the training data, unusually large size of training data, and noise in the class labels of the training data.

My most highly-cited paper in this area introduced the synthetic minority over-sampling technique, or "SMOTE". SMOTE is an approach to dealing with class imbalance in the training data by creating additional synthetic training samples. (code for example SMOTE implementation)

Medical Image Analysis.
I previously worked in several areas of medical image analysis, including detection of signs of cancer in mammograms, algorithms for estimating shunted blood flow in the heart, and CT stereotaxis calculations used by neurosurgeons operating on the brain.

Object Recognition.
I made a series of contributions in two areas of object recognition: object recognition through reasoning about functionality and aspect graphs for object representation.


A list of recent and selected publications is available, with links to pdf versions of most. My Google Scholar "h index" is currently in the mid 60s; see Google Scholar profile.

Click on the image on the right for details and ordering info. The Handbook was reviewed in ACM Computing Reviews by Creed Jones (review #: CR141221). The closing paragraph of the review - "In the interest of testing this excellent volume, I posed several questions about iris recognition and attempted to find the answers within. I was pleased to see that either relevant information was present and quickly locatable, or there were sufficient references for further study. Since the preface claims that this is "the first book to be devoted entirely to iris recognition," it should be as accurate and complete as a reasonable length allows. It succeeds on both counts. For anyone interested in iris recognition, this book is indispensable."

PhD graduates.

A list of the twenty-four wonderful colleagues that I have had the pleasure of working with in the past as PhD students is available, with links to some of the their dissertations.

Two of my PhD graduates, Deborah Thomas and Karen Hollingsworth, appear in the picture to the left, taken after the graduate school graduation ceremony.

Graduate students currently working with me include Sandipan Banerjee, John Bernhard, Aparna Bharati, Joel Brogan, Andrey Kuehlkamp, and Juan Tapia (research visitor from University of Chile).

Congratulations to Estefan Ortiz, Amanda Sgroi and to Jay Doyle on their recent successful PhD completions!

My research has been supported by a number of organizations, including NSF, IARPA, FBI, CIA, TISWG, DARPA, Sandia Labs and others.