Mr. Harish Srinivasan Profile

Harish Srinivasan

at Janya Inc

SPIE Involvement:

Author

Publications (10)

Proceedings Article | 24 January 2012 Paper

Questioned document workflow for handwriting with automated tools

Krishnanand Das, Sargur Srihari, Harish Srinivasan

Proceedings Volume 8297, 82970K (2012) https://doi.org/10.1117/12.912104

KEYWORDS: Forensic science, Image segmentation, Distance measurement, Image processing, Binary data, Feature extraction, Roads, Computational forensics, Current controlled current source, Distortion

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Proceedings Article | 24 January 2012 Paper

Handwriting individualization using distance and rarity

Yi Tang, Sargur Srihari, Harish Srinivasan

Proceedings Volume 8297, 82970R (2012) https://doi.org/10.1117/12.912035

KEYWORDS: Lawrencium, Binary data, Forensic science, Distance measurement, Statistical analysis, Statistical modeling, Error analysis, Defense and security, Feature extraction, Data modeling

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Proceedings Article | 28 January 2008 Paper

Robust line segmentation for handwritten documents

Kamal Kuzhinjedathu, Harish Srinivasan, Sargur Srihari

Proceedings Volume 6815, 68150C (2008) https://doi.org/10.1117/12.766701

KEYWORDS: Image segmentation, Detection and tracking algorithms, Spine, Image processing algorithms and systems, Statistical methods, Artificial neural networks, Image processing, Electronic imaging, Neurons, Image quality

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Proceedings Article | 12 April 2007 Paper

Use of ridge points in partial fingerprint matching

Gang Fang, Sargur Srihari, Harish Srinivasan, Prasad Phatak

Proceedings Volume 6539, 65390D (2007) https://doi.org/10.1117/12.718941

KEYWORDS: Databases, Biometrics, Forensic science, Statistical modeling, Performance modeling, Tolerancing, Computer simulations, Biological research, Fingerprint recognition, Detection and tracking algorithms

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Proceedings Article | 29 January 2007 Paper

A statistical approach to line segmentation in handwritten documents

Manivannan Arivazhagan, Harish Srinivasan, Sargur Srihari

Proceedings Volume 6500, 65000T (2007) https://doi.org/10.1117/12.704538

KEYWORDS: Image segmentation, Detection and tracking algorithms, Image processing algorithms and systems, Switches, Mirrors, Statistical analysis, Electronic imaging, Binary data, Matrices, Statistical modeling

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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 | 16 January 2006 Paper

Spotting words in handwritten Arabic documents

Sargur Srihari, Harish Srinivasan, Pavithra Babu, Chetan Bhole

Proceedings Volume 6067, 606702 (2006) https://doi.org/10.1117/12.643107

KEYWORDS: Image segmentation, Prototyping, Databases, Binary data, Human-machine interfaces, Neural networks, Image retrieval, Image processing algorithms and systems, Image processing, Associative arrays

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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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Proceedings Article | 17 January 2005 Paper

Search engine for handwritten documents

Sargur Srihari, Chen Huang, Harish Srinivasan

Proceedings Volume 5676, (2005) https://doi.org/10.1117/12.585883

KEYWORDS: Image segmentation, Feature extraction, Image retrieval, Distance measurement, Binary data, Databases, Forensic science, Document management, Visualization, Digital libraries

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Showing 5 of 10 publications

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