Dr. Matthew J. Beal Profile

Dr. Matthew J. Beal

at Univ at Buffalo

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Publications (3)

Proceedings Article | 29 January 2007 Paper

Segmentation and labeling of documents using conditional random fields

Shravya Shetty, Harish Srinivasan, Matthew Beal, Sargur Srihari

Proceedings Volume 6500, 65000U (2007) https://doi.org/10.1117/12.704410

KEYWORDS: Image segmentation, Data modeling, Nickel, Neural networks, Feature extraction, Image retrieval, Performance modeling, Lithium, Image processing algorithms and systems, Neurons

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Proceedings Article | 17 April 2006 Paper

Comparison of ROC-based and likelihood methods for fingerprint verification

Harish Srinivasan, Sargur Srihari, Matthew Beal, Prasad Phatak, Gang Fang

Proceedings Volume 6202, 620209 (2006) https://doi.org/10.1117/12.665596

KEYWORDS: Statistical modeling, Error analysis, Databases, Feature extraction, Biometrics, Forensic science, Distance measurement, Image quality, Receivers, Statistical analysis

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The fingerprint verification task answers the question of whether or not two fingerprints belongs to the same finger. The paper focuses on the classification aspect of fingerprint verification. Classification is the third and final step after after the two earlier steps of feature extraction, where a known set of features (minutiae points) have been extracted from each fingerprint, and scoring, where a matcher has determined a degree of match between the two sets of features. Since this is a binary classification problem involving a single variable, the commonly used threshold method is related to the so-called receiver operating characteristics (ROC). In the ROC approach the optimal threshold on the score is determined so as to determine match or non-match. Such a method works well when there is a well-registered fingerprint image. On the other hand more sophisticated methods are needed when there exists a partial imprint of a finger- as in the case of latent prints in forensics or due to limitations of the biometric device. In such situations it is useful to consider classification methods based on computing the likelihood ratio of match/non-match. Such methods are commonly used in some biometric and forensic domains such as speaker verification where there is a much higher degree of uncertainty. This paper compares the two approaches empirically for the fingerprint classification task when the number of available minutiae are varied. In both ROC-based and likelihood ratio methods, learning is from a general population of ensemble of pairs, each of which is labeled as being from the same finger or from different fingers. In the ROC-based method the best operating point is derived from the ROC curve. In the likelihood method the distributions of same finger and different finger scores are modeled using Gaussian and Gamma distributions. The performances of the two methods are compared for varying numbers of minutiae points available. Results show that the likelihood method performs better than the ROC-based method when fewer minutiae points are available. Both methods converge to the same accuracy as more minutiae points are available.

Proceedings Article | 20 May 2005 Paper

Machine learning approaches for person identification and verification

Harish Srinivasan, Matthew Beal, Sargur Srihari

Proceedings Volume 5778, (2005) https://doi.org/10.1117/12.601987

KEYWORDS: Error analysis, Biometrics, Machine learning, Distance measurement, Feature extraction, Binary data, Forensic science, Homeland security, Statistical modeling, Databases

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