PhD Defense - Jeffrey Paone
|Start:||6/19/2013 at 9:00AM|
|End:||6/19/2013 at 1:00PM|
|Location:||384 Fitzpatrick Hall|
Jeffrey Paone, a Computer Science and Engineering PhD candidate, will be presenting and defending his dissertation; "Liberating the Biometric Menagerie Through Score Normalization Improvements" on June 19th, at 9:00 am in 100 Stinson Remick.
His advisor Dr. Patrick Flynn, and committee members Dr. Kevin Bowyer, Dr. Nitesh Chawla and Dr. Jesus Izaguirre will be in attendance. Students and faculty are welcome to attend the presentation portion of the defense. Light refreshments will be served.
The biometric menagerie, or biometric zoo, is a classification system used to label the matching tendencies of a given subject’s biometric signature. These tendencies may include matching themselves poorly or matching other subjects better than themselves. Several experiments show the biometric menagerie to be an unstable classification system where subjects frequently change class la- bels. In an attempt to improve the stability of the biometric menagerie, existing score normalization techniques are expanded to create Covariate F-Normalization (CovF-Norm). When the normalization methods are applied to the biometric menagerie, the classification system remains unstable and unreliable for practical use with subject-specific thresholding. The new normalization method, CovF- Norm, is also shown to be algorithm independent and data set independent unlike the biometric menagerie which is dependent on both the algorithm and data set. CovF-Norm is shown to significantly improve performance when compared to the standard F-Normalization technique’s equal error rate.