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Venu Govindaraju

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Venu Govindaraju

SUNY Distinguished Professor
Department Computer Science and Engineering
School of Engineering and Applied Sciences
113 Davis Hall
University at Buffalo, Amherst, NY 14260-2500

Phone: 716-645-1558; Fax: 716-645-2377
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Linkedin:Linkedin Profile www.linkedin.com/pub/venu-govindaraju/5/72/195
Area: Machine Learning, Biometrics, Language Technologies

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Dr. Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the founding director of the Center for Unified Biometrics and Sensors. He received his Bachelor’s degree with honors from the Indian Institute of Technology (IIT) in 1986, and his Ph.D. from UB in 1992. His research focus is on  machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics.

Dr. Govindaraju has co-authored about 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service. He was also the prime technical lead responsible for technology transfer to the Postal Services in US, Australia, and UK. He has been a Principal or Co-Investigator of sponsored projects funded for about 65 million dollars.

Dr. Govindaraju has supervised the dissertations of 30 doctoral students. He has served on the editorial boards of premier journals such as the IEEE Transactions on Pattern Analysis and Machine Intelligence and is currently the Editor-in-Chief of the IEEE Biometrics Council Compendium.

Dr. Govindaraju is a Fellow of the ACM (Association of Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition), and the SPIE (International Society of Optics and Photonics). He is recipient of the 2004 MIT Global Indus Technovator award and the 2010 IEEE Technical Achievement award.

Awards

Govindaraju is a Fellow of the Association for Computing Machinery,[5] the IEEE (Institute of Electrical and Electronics Engineers),[6] the AAAS (American Association for the Advancement of Science),[7] the IAPR (International Association of Pattern Recognition),[8] and the SPIE (International Society of Optics and Photonics).[9]

He is the recipient of the 2001 International Conference on Document Analysis and Recognition Young Investigator award,[10] the 2004 MIT Global Indus Technovator Award,[11] the 2010 IEEE Technical Achievement Award,[12] and the Indian Institutes of Technology (IIT) Distinguished Alumnus Award(2014).

 

Honors

•Fellow: ACM, IEEE, AAAS, IAPR, SPIE
•IEEE Technical Achievement
•MIT Global Indus Technovator
•SUNY Chancellor’s Award
•ICDAR Outstanding Young Investigator Award

Industry Awards

•HP Open Innovation
•IBM Faculty Research
•Google Faculty Research
•eBay Faculty Research
•Fujitsu Faculty Research

 

Research Career

Govindaraju is a SUNY Distinguished Professor in the Department of Computer Science and Engineering at the University at Buffalo, The State University of New York.[13] He is the Associate Director of the Center of Excellence for Document Analysis and Recognition (CEDAR) since 1995 and the founding director of the Center for Unified Biometrics and Sensors (CUBS) since its inception in 2003.

Publication record: Govindaraju has authored 5 edited books and 400 refereed publications[14] that include 78 journal papers, 22 book-chapters, and 300 symposium/conference papers in pattern recognition theory and its applications.

Edited Books (5):

  • Handbook of Statistics: Big Data, V. Govindaraju, Vijayraghavan, and CR Rao (eds.), Elsevier 2015 (in print).
  • Handbook of Statistics: Machine Learning Theory and Applications, C. R. Rao & V. Govindaraju (eds.), Elsevier 2013.
  • Multibiometrics for Human Identification, B. Bhanu & V. Govindaraju (eds.), Cambridge University Press 2011.
  • Indic OCR- Document Recognition & Retrieval, V. Govindaraju & S. Setlur (eds.), Springer 2009.
  • Biometrics: Sensors, Systems, and Algorithms, N. Ratha & V. Govindaraju (eds.), Springer 2007.

Patents Awarded:

  1. US 5,515,455: "System for recognizing handwritten words of cursive script", V. Govindaraju; D. Wang; and S. Srihari, 1996.[15]
  2. US 7,580,551. "Method and apparatus for analyzing and/or comparing handwritten and/or biometric samples", S. Srihari; V. Govindaraju; et. al. 2009.[16]
  3. US 7,689,006: "Biometric convolution using multiple biometrics", V. Govindaraju; V. Chavan; and S. Chikkerur, 2010.[17]
  4. US 8,005,277: "Secure fingerprint matching by hashing localized information", S. Tulyakov; F. Farooq; S. Chikkerur; and V. Govindaraju, 2011.[18]

Student advisement and mentoring: Govindaraju has supervised 32 Ph.D theses and 15 MS theses as major advisor.

Professional Service

Editorial: Govindaraju serves on the editorial board of 10 premier journals including 3 IEEE transactions (IEEE-T-PAMI IEEE-T-SMC, IEEE-T-IFS) and IEEE Access. He is also the editor-in-chief of the IEEE Biometrics Council Compendium.[19]

Conferences: Govindaraju has been the General (Co)Chair at 12 conferences/workshops including International Conference on Document Analysis and Recognition (ICDAR), Program (Co) Chair in 14 conferences/workshops including Biometrics: theory, Algorithms and Systems (BTAS) , and program chairs of numerous conferences that span both document analysis and biometrics areas.

Speaking: Govindaraju has been an invited speaker at the NRC Intelligence Committee Workshop on Science & Tech Investments where he presented on the topic of “Accelerated Discovery in the Era of Scientific Information Overload”.

Public service: Govindaraju has been on the advisory board of the Buffalo Niagara Enterprise, EngageClick Inc., Copanion Inc., and the International Graphonomics Society. He is currently the President of the IEEE Biometrics Council (2015–2017).[20]

Technical Accomplishments

Govindaraju’s research has focused on the application of machine learning and pattern recognition techniques to domains such as Document Analysis and Recognition and Biometrics and, in particular, the development of real-time engineered systems.

He has developed principled modeling approaches for pattern classification that have resulted in the development of robust, scalable systems in a variety of application domains, from document processing to biometrics. He has designed several algorithms for cursive handwriting recognition suitable for real time applications that demonstrated the benefits of innovative modeling of application constraints. His language-motivated hierarchical modeling has been extended to computer vision applications such as scene understanding and classifying activities and gestures in unconstrained videos. He has also made contributions to the theoretical foundations of a general fusion architecture and taxonomy of trained combining functions (classifiers) and their input parameters which provides a principled guideline for choosing a particular fusion technique.

Engineered Systems

usps-poster-4

Govindaraju's work in handwriting recognition[22][23] was at the core of the first handwritten address interpretation system used by the United States Postal Service (USPS). The learning-based system that he developed as project technical lead along with his colleagues at the University at Buffalo helps save the USPS hundreds of millions of dollars by automatically processing, and barcoding for precise delivery, over 25 billion letters a year.[24]

This work was highlighted in the Computing Community Consortium's symposium on Computing Research that Changed the World in 2009 as one of the most successful applications of Machine Learning for developing a real-time engineered system.[25][26]

The Government Executive publication reported in 1999 that "USPS issued a contract to researchers at the State University of New York at Buffalo to develop the handwriting recognition technology. It was first launched in 1997 right before the Christmas holiday season. One year later, an estimated 400 million pieces of mail were automatically routed during the Christmas season alone using the handwriting recognition technology. The new technology has saved the Postal Service at least $90 million in its first year in the field."[27]

USPS Engineering Vice President William J. Dowling singled out Lockheed Martin and its suppliers, the State University of New York at Buffalo, and Parascript, LLP, for their work in improving RCR performance.[28] Edward Kuebert, manager of image and telecommunications technology at USPS also credited improvements in reader technology to the State University of New York at Buffalo, Lockheed Martin Federal Systems and the Parascript Group of Boulder, Colo.[29]

Pattern Recognition Techniques

Govindaraju developed a suite of efficient and field-tested image processing routines for handling the contour representation in handwritten word images.[30][31] Departing from the myriad of heuristic approaches, he introduced a statistical approach to binarization and noise removal by modeling the degraded document as a Markov Random Field (MRF) where the prior is learnt from a training set of high quality images, and the probabilistic density is learnt on-the-fly.[32]

Govindaraju modeled “active” recognition along the lines of the A* algorithm.[33] This method provides a multi-resolution framework for adapting to factors such as the quality of the input pattern, its intrinsic similarities with patterns of other classes, and the processing time available. Finer resolution is accorded to only certain “zones” of the input pattern which are deemed information bearing given the classes that were being discriminated.

Govindaraju has contributed to the combination (fusion) of pattern classifiers and proved that the optimal combination algorithm for identification systems is difficult to express analytically because of the difficulty presented by the dependencies between matching scores assigned to different classes by the same classifier.[34] He developed the first taxonomy of the complexity of classification combination methodoogies and a guideline for choosing a particular type of fusion technique.[35]

Document Recognition and Retrieval

Handwriting Recognition Models

Govindaraju developed the first handwritten word recognition module suitable for real time applications[36] using an innovative dynamic matching algorithm to assign automatically segmented pieces of words to lexical entities. Prior methods modeled either discrete features or continuous features but not both. The early models were extended to a new stochastic framework which modeled sequences of features that combined discrete symbols and continuous attributes.[37]

Govindaraju has incorporated the theories of reading and perception developed in psychology literature in analyzing handwritten words[38] and demonstrated its uses in postal address reading.[39] He has also contributed to improvement in word recognition accuracy of unconstrained handwritten documents by applying OCR correction techniques[40] in a bootstrapping framework where innovative topic categorization techniques are used to generate smaller topic-specific lexicons.[41]

His work on multi-lingual OCR spans recognition of Arabic[42] and Devanagari script.[43] His book on the OCR of Indic Scripts[44] is the first comprehensive book on this subject. Govindaraju developed a performance model that statistically "discovers" the relation between a word recognizer and the lexicon. It uses model parameters that capture a recognizer's ability of distinguishing characters and its sensitivity to lexicon size. Such a model is useful in comparing word recognizers by predicting their performance based on the lexicon presented. He described the notion of “lexicon density” as a metric to measure the expected accuracy of handwritten word recognizers.[45][46]

Document Analysis Systems

Govindaraju authored a widely-cited system-level paper describing the architecture of an end-to-end system for reading unconstrained handwritten page images.[47] Prior research in handwriting recognition treated phrases as a concatenation of the constituent words. He presented a methodology that took advantage of the spacing between the words by modeling the word breaks as a feature of the individual writing style.[48][49] He developed a bank check reading system that could leverage the recognition of the legal amounts (written in long hand) in the decision making process along with the existing and more accurate recognition of courtesy amounts (numeric strings).[50] He enabled recognition of medical forms by modeling the relationships between handwriting and medical topics. This technique showed that a few automatically recognized characters can be used to construct a linguistic model capable of representing a medical topic category.[51]

Innovative Applications

Govindaraju developed the first simulation of human-like handwriting for designing CAPTCHAs to exploit the differential in handwriting reading proficiency between humans and machines.[52] The MIT Tech Review (Jan. 2009) highlighted the ingenuity of his Spambot-Fighting Strategy[53] which is grounded in cognitive science principles.

He has developed methods that stochastically model imperfect word segmentation inherent in handwriting[54] and techniques for transcript mapping[55] useful for indexing handwritten documents.

Biometrics

Fingerprint Templates and Matching

Govindaraju has explored enhancement and binarization techniques for fingerprint images: Fourier analysis,[56] direction median filters,[57] Harris points,[58] fingerprint quality measures.[59] He proposed a minutia extraction algorithm utilizing the contours of minutia ridges[60] and matching based on features extracted from minutia k-plets and formulated the matching algorithm as a minimum cost flow problem[61] and coupled breadth-first search algorithm.[62] The fingerprint templates stored in biometric databases typically contain the original sensor data, or features, from which the intruder can potentially reconstruct fake biometric samples. Govindaraju invented a unique fingerprint hashing method[63][64] where only hashes are transmitted and stored in the database, and it is not possible to restore original biometric data from them. He proposed a novel indexing method based on minutia k-plet paths[65] whose search time remains constant even when increasing number of enrolled persons.

Face and Facial Expression Analysis

Govindaraju proposed one of the earliest model-based face recognition methods[66][67] and developed a face matching system based on semantic descriptors.[68] He proposed a hybrid facial feature localization method based on graphical models and image sampling[69] and verified the individuality of facial expressions, demonstrating that either displacements of facial features[70] or the frequencies of particular expressions[71] could be used as biometric modalities. He innovated methods for automated detection of deceit in facial expressions using changes in facial geometry, texture[72] and changes in the eye movements.[73]

Smart Environments

Govindaraju formulated a probabilistic framework for person identification and tracking in smart environments consisting of a set of connected rooms[74] wherein multiple biometric modalities are coupled with the probabilistic track model in this framework. He investigated the concepts of computer security and online user behavior[75][76] and introduced methods for confirming the identity of online users.[77][78]

Writer Identification

Govindaraju has proposed that, although handwriting is unique to writers, writer style represents a shared component of individual handwriting.[79] He explicitly models this conceptualization via a three-level hierarchical Bayesian framework (LDA) for the purposes of writer identification and verification.[80][81] In this text-independent model, each writer's handwriting is modeled as a distribution over a limited set of writing styles that are shared amongst writers. He has shown that, analogous to speech, accents in writing are treated as distinctive quirks unique to a group of people belonging to a common family of scripts which have roots in cultural and genetic factors.[82][83]

 

Impactful Papers

  • V. Govindaraju, "Locating human faces in photographs", The International Journal of Computer Vision, Kluwer Academic Publishers, 19(2): 129-146 (1996)[84]
  • G. Kim, V. Govindaraju, “A lexicon driven approach to handwritten word recognition for real-time applications”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 19(4): 366-379 (1997)[85]
  • S. Madhvanath, E. Kleinberg*, and V. Govindaraju, “Holistic verification of handwritten phrases", IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 21(12): 1344-1356 (1999)[86]
  • V. Govindaraju, K.G. Ianakiev, “Potential improvement of classifier accuracy by using fuzzy measures”, IEEE Transactions Fuzzy Systems, IEEE Computational Intelligence Society Press, 8(6): 679-690 (2000)[87]
  • S. Madhvanath, V. Govindaraju, “The role of holistic paradigms in handwritten word recognition”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 23(2): 149-164 (2001)[88]
  • H. Xue, V. Govindaraju, “On the dependence of handwritten word recognizers on lexicons”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 24(12): 1553-1564 (2002)[89]
  • V. Govindaraju, P. Slavik*, and H. Xue, “Use of lexicon density in evaluating word recognizers”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 24(6): 789-800 (2002)[90]
  • H. Lei and V. Govindaraju, "A Comparative Study on the Consistency of Features in On-line Signature Verification", Pattern Recognition Letters, Elsevier Science, 26(15): 2483-2489 (2005)[91]
  • H. Xue, V. Govindaraju, “Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 28(3): 458-462 (2006)[92]
  • S. Chikkerur, A. Cartwright, and V. Govindaraju, "Fingerprint Image Enhancement Using STFT Analysis", The Journal of Pattern Recognition, Elsevier Publishers, 40(1):198-211 (2007)[93]
  • S. Tulyakov, V. Govindaraju, "Use of identification trial statistics for the combination of biometric matchers"], IEEE Transactions on Information Forensics and Security, IEEE Signal Processing Society Press, 3(4): 719-733 (2008)[94]
  • H. Cao, A. Bhardwaj, V. Govindaraju, “A probabilistic method for keyword retrieval in handwritten document images”, Pattern Recognition Journal, Elsevier Press, 42(12): 3374-3382 (2009)[95]
  • S. Kompalli, S. Setlur, and V. Govindaraju, “Devanagari OCR using a recognition driven segmentation framework and stochastic language models”, International Journal of Document Analysis and Recognition, Springer Press, 11(2): 203-218 (2009)[96]
  • H. Cao, V. Govindaraju, “Preprocessing of low quality handwritten documents using Markov random fields”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 31(7): 1184-1194 (2009)[97]
  • S. Tulyakov, C. Wu, and V. Govindaraju, “On the difference between optimal combination functions for verification and identification systems”, International Journal Pattern Recognition and Artificial Intelligence, World Scientific Press, 24(2): 173-191 (2010)[98]
  • Y. Zhou, I. Nwogu, and V. Govindaraju, “Labeling Spain with Stanford”, IEEE Transactions on Image Processing, IEEE Signal Processing Society Press, 22(12): 5362-5371 (2013)[99]
  • M. Malgireddy, I. Nwogu, and V. Govindaraju, “Language motivated approach to action recognition”, Journal of Machine Learning Research, MIT Press, 14:2189−2212 (2013)[100]

References

  1. ↑ Office of the Vice President of Research and Economic Development
  2. ↑ UB Reporter: Govindaraju named interim vice president for research and economic development By Cory Nealon
  3. ↑ University at Buffalo: Govindaraju named SUNY Distinguished Professor By Ellen Goldbaum
  4. ↑ Govindaraju PhD Tree from the Mathematics Genealogy Project
  5. ↑ ACM Fellow
  6. ↑ IEEE Fellow
  7. ↑ AAAS Fellow
  8. ↑ IAPR Fellow
  9. ↑ SPIE Fellow
  10. ↑ ICDAR Young Investigator Award
  11. ↑ MIT Globus Indus Technovator
  12. ↑ IEEE Technical Achievement Award
  13. ↑ Center for Unified Biometrics and Sensors, Venu Govindaraju CV
  14. ↑ Venu Govindaraju - Google Scholar Profile
  15. ↑ US 5,515,455: “System for recognizing handwritten words of cursive script”
  16. ↑ US 7,580,551: "Method and apparatus for analyzing and/or comparing handwritten and/or biometric samples"
  17. ↑ US 7,689,006: "Biometric convolution using multiple biometrics"
  18. ↑ US 8,005,277: "Secure fingerprint matching by hashing localized information"
  19. ↑ IEEE Biometrics Council Compendium
  20. ↑ IEEE Biometrics Council President
  21. ↑ "Computing Research that Changed the World", Computing Community Consortium
  22. ↑ G. Kim and V. Govindaraju, "A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications", IEEE TPAMI 19(4):366-379, 1997
  23. ↑ S. Madhvanath, E. Kleinberg, and V. Govindaraju, "Holistic Verifcation of Handwritten Phrases", IEEE TPAMI 21(12):1344-1356, 1999
  24. ↑ Daphne Koller, Learning to Improve our Lives, Computing Community Consortium, March 2009
  25. ↑ Daphne Koller, Learning to Improve our Lives, Computing Community Consortium, March 2009
  26. ↑ "Computing Research that Changed the World", Computing Community Consortium
  27. ↑ Saldarini, Katy: "Postal Service tests handwriting recognition system", Government Executive, February 1, 1999
  28. ↑ Drake, Michael: "Lockheed Martin provides USPS increased letter mail address recognition capability", June 10, 1999
  29. ↑ Mayer, Merry: "Postal Service's upgrades of electronic mail readers exceed target goals", GCN, July 30, 1999
  30. ↑ S. Madhvanath, G. Kim, and V. Govindaraju, “Chain code processing for handwritten word recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 21(9): 928-932, 1999)
  31. ↑ P. Slavik and V. Govindaraju, “Equivalence of methods for slant and skew correction in word recognition applications”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 23(3): 323-325, 2001
  32. ↑ H. Cao and V. Govindaraju, “Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields”, IEEE Trans. Pattern Anal. Mach. Intell. 31(7): 1184-1194 (2009)
  33. ↑ J. Park, V. Govindaraju, and S. Srihari, “OCR in a hierarchical feature space”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 22(4): 400-406, 2000
  34. ↑ S. Tulyakov, C. Wu, and V. Govindaraju, “On the difference between optimal combination functions for verification and identification systems”, International Journal Pattern Recognition and Artificial Intelligence, 24(2): 173-191, 2010
  35. ↑ S. Tulyakov and V. Govindaraju, “Use of identification trial statistics for combination of biometric matchers”, IEEE Transactions on Information Forensics and Security, IEEE Signal Processing Society Press, 3(4): 719-733, 2008
  36. ↑ G. Kim and V. Govindaraju, “A lexicon driven approach to handwritten word recognition for real- time applications”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 19(4): 366-379, 1997
  37. ↑ H. Xue and V. Govindaraju, “A stochastic model combining discrete symbols and continuous attributes in handwriting recognition", IEEE Transaction on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 28(3): 458-462, 2006
  38. ↑ S. Madhvanath and V. Govindaraju, “The role of holistic paradigms in handwritten word recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 23(2): 149-164, 2001
  39. ↑ S. Madhvanath, E. Kleinberg, and V. Govindaraju, “Holistic verification of handwritten Phrases”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 21(12):1344-1356, 1999
  40. ↑ F. Farooq, D. Jose, and V. Govindaraju, “Phrase based direct model for improving handwriting recognition accuracies”, The Journal of Pattern Recognition, Special Issue on Handwriting Recognition, Elsevier Press, 42(12): 2009
  41. ↑ F. Farooq, A. Bharadwaj, and V. Govindaraju, “Using topic models for OCR correction”, International Journal of Document Analysis and Recognition, Springer, 12(3): 153-164, 2009
  42. ↑ L. Lorigo and V. Govindaraju, “Offline arabic handwritten word recognition: A survey”, IEEE Transaction on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 28(5): 712-724, 2006
  43. ↑ Suryaprakash Kompalli, Srirangaraj Setlur, and Venu Govindaraju, “Devanagari OCR using a recognition driven segmentation framework and stochastic language models”, IJDAR 12(2): 123-138 (2009)
  44. ↑ Indic OCR- Document Recognition & Retrieval. V. Govindaraju & S. Setlur (eds.). Springer 2009
  45. ↑ V. Govindaraju, P. Slavik, and H. Xue, “Use of lexicon density in evaluating word recognizers”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 24(6): 789-800, 2002
  46. ↑ H. Xue and V. Govindaraju, “On the dependence of handwritten word recognizers on lexicons”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 24(12): 1553-1564, 2002
  47. ↑ G. Kim, V. Govindaraju, and S. Srihari, “An architecture for handwritten text recognition systems” International Journal of Document Analysis and Recognition, Springer Verlag, 2(1): 37-44, 1999
  48. ↑ G. Kim and V. Govindaraju, “Handwritten phrase recognition as applied to street name images”, Journal of Pattern Recognition, Pergamon Press, 31(1): 41-51, 1998
  49. ↑ J. Park and V. Govindaraju, “Use of adaptive segmentation in phrase recognition”, The Journal of Pattern Recognition, Pergamon Publishers, 35(1): 245-252, 2002
  50. ↑ G. Kim and V. Govindaraju, “Bank check recognition using cross validation between legal and courtesy amounts” International Journal on Pattern Recognition and Artificial Intelligence, World Scientific Publishing Company, 11(4): 657-674, 1997
  51. ↑ R. Milewski, A. Bharadwaj, and V. Govindaraju, “Automatic recognition of handwritten medical forms for search engines”, International Journal of Document Analysis and Recognition, Springer, 11: 203-218, 2009
  52. ↑ A. Thomas, A. Rusu, and V. Govindaraju, “Synthetic handwritten CAPTCHAs”, The Journal of Pattern Recognition, Special Issue on Handwriting Recognition, Elsevier Press, 42(12): 3365-3373, 2009
  53. ↑ Duncan Graham-Rowe, "A Joined-Up Bot-Fighting Strategy", MIT Technology Review, January/February 2009
  54. ↑ H. Cao, A. Bharadwaj, and V. Govindaraju, “A probabilistic method for keyword retrieval in handwritten document images”, The Journal of Pattern Recognition, Special Issue on Handwriting Recognition, Elsevier Press, 42(12): 3374-3382, 2009)
  55. ↑ C. Tomai, B. Zhang, and V. Govindaraju, “Transcript mapping for historic handwritten documents”, 8th International Workshop on Frontiers of Handwriting Recognition, IEEE Computer Society Press, Niagara-on-the-Lake, Canada, pp. 413-418, August 2002
  56. ↑ S. Chikkerur, A. Cartwright, and V. Govindaraju, “Fingerprint image enhancement using STFT analysis”, The Journal of Pattern Recognition, Elsevier Publishers, 40(1): 198-211, 2007
  57. ↑ Tsai-Yang Jea, Viraj S. Chavan, Venu Govindaraju and John K. Schneider "Security and matching of partial fingerprint recognition systems", Proc. SPIE 5404, Biometric Technology for Human Identification, 39 (August 25, 2004)
  58. ↑ "Robust Point-Based Feature Fingerprint Segmentation Algorithm", Chaohong Wu, Sergey Tulyakov, Venu Govindaraju
  59. ↑ "Image Quality Measures for Fingerprint Image Enhancement", Chaohong Wu, Sergey Tulyakov, Venu Govindaraju
  60. ↑ Zhixin Shi, Venu Govindaraju: A chaincode based scheme for fingerprint feature extraction. Pattern Recognition Letters 27(5): 462-468 (2006)
  61. ↑ T. Jea and V. Govindaraju: A minutia-based partial fingerprint recognition system. The Journal of Pattern Recognition, Elsevier Publishers, 38(10): 1672-1684, 2005
  62. ↑ "K-plet and Coupled BFS: A Graph Based Fingerprint Representation and Matching Algorithm", Sharat Chikkerur, Alexander N. Cartwright, Venu Govindaraju
  63. ↑ S. Tulyakov, F. Farooq, P. Mansukhani, and V. Govindaraju, “Symmetric hash functions for securing fingerprint templates”, Pattern Recognition Letters, Elsevier Publishers, 28(16): 2427-2436, 2007
  64. ↑ US Patent 8,005,277
  65. ↑ P. Mansukhani, S. Tulyakov, and V. Govindaraju, “A framework for efficient fingerprint identification using a minutiae tree”, IEEE Systems Journal- Special Issue on Biometrics, 4(2): 126-137, 2010
  66. ↑ V. Govindaraju, D. Sher, and S. Srihari, “A computational model for face location”, 3rd International Conference on Computer Vision, IEEE Computer Society Press, pp. 718-721, Osaka, Japan, December 1990
  67. ↑ V. Govindaraju, “Locating human faces in photographs”, The International Journal of Computer Vision, Kluwer Academic Publishers, 19(2): 129-146, 1996
  68. ↑ K. Sridharan, S. Nayak, S. Chikkerur and V. Govindaraju, “'A probabilistic approach to semantic face retrieval system”, International Conference on Audio and Video Based Biometric Person Authentication, 977-986, Tarrytown, NY, 2005
  69. ↑ K. Sridharan and V. Govindaraju, “A sampling based approach to facial feature extraction”, 4th IEEE Workshop on Automatic Identification Advanced Technologies (AutoID), pp. 51-56, Buffalo, NY, 2005.
  70. ↑ Tulyakov, S.; Slowe, T.; Zhi Zhang; Govindaraju, V., "Facial Expression Biometrics Using Tracker Displacement Features," Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on , vol., no., pp.1,5, 17-22 June 2007
  71. ↑ Kashyap, A.L.; Tulyakov, S.; Govindaraju, V., "Facial behavior as a soft biometric," Biometrics (ICB), 2012 5th IAPR International Conference on , vol., no., pp.147,151, March 29 2012-April 1 2012
  72. ↑ Zhi Zhang; Singh, V.; Slowe, T.E.; Tulyakov, S.; Govindaraju, V., "Real-time Automatic Deceit Detection from Involuntary Facial Expressions," Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on , vol., no., pp.1,6, 17-22 June 2007
  73. ↑ N. Bhaskaran, I. Nwogu, M. Frank, and V. Govindaraju, “Lie To Me: Deceit detection via online behavioral learning”, 9th IEEE Conference on Face and Gesture Recognition, Santa Barbara, CA, 2011
  74. ↑ V. Menon, B. Jayaraman, and V. Govindaraju, “Architecture for multimodal identification and tracking in smart environments”, Special Issue on Multimodal Systems, Services and Interfaces for Ubiquitous Computing in the Journal of Personal and Ubiquitous Computing, Springer, 14(8) 685- 694, 2010, IEEE Computer, 44(9), 73-79(2011), PUC (2012)
  75. ↑ [IJBM: 1,81-113(2008)]
  76. ↑ R. V. Yampolskiy and V. Govindaraju, “Computer security: A survey of methods and systems”,Journal of Computer Science, 3(7): 478-486, 2007
  77. ↑ R. V. Yampolskiy and V. Govindaraju. Direct and indirect human computer interaction based biometrics. Journal of Computers, 2(8) 76-88, 2007
  78. ↑ R. V. Yampolskiy and V. Govindaraju, “Behavioral biometrics classification”, International Journal of Biometrics, Inder science Publishers, 35(1): 29-41, 2008
  79. ↑ A. Shivaram, C. Ramiah, U. Poruwal, and V. Govindaraju, “Modeling writing styles for online writer identification: A hierarchical Bayesian approach”, 13th International Conference on Frontiers of Handwriting Recognition (ICFHR), Bari, Italy, 2012
  80. ↑ U. Porwal, and V. Govindaraju, “Semi supervised framework for writer identification using structural learning”, IET Biometrics, 2(4): 208-215, 2013
  81. ↑ A. Shivaram, C. Ramiah, U. Poruwal, and V. Govindaraju, “Modeling writing styles for online writer identification: A hierarchical Bayesian approach”, 13th International Conference on Frontiers of Handwriting Recognition (ICFHR), Bari, Italy, 2012
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  83. ↑ Ramaiah, C.; Shivram, A.; Govindaraju, V., "A Bayesian Framework for Modeling Accents in Handwriting," Document Analysis and Recognition (ICDAR), 2013 12th International Conference on , vol., no., pp.917,921, 25-28 Aug. 2013
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  85. ↑ G. Kim, V. Govindaraju, “A lexicon driven approach to handwritten word recognition for real-time applications”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 19(4): 366-379 (1997)
  86. ↑ S. Madhvanath, E. Kleinberg*, and V. Govindaraju, “Holistic verification of handwritten phrases", IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 21(12): 1344-1356 (1999)
  87. ↑ V. Govindaraju, K.G. Ianakiev, “Potential improvement of classifier accuracy by using fuzzy measures”, IEEE Transactions Fuzzy Systems, IEEE Computational Intelligence Society Press, 8(6): 679-690 (2000)
  88. ↑ S. Madhvanath, V. Govindaraju, “The role of holistic paradigms in handwritten word recognition”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 23(2): 149-164 (2001)
  89. ↑ H. Xue, V. Govindaraju, “On the dependence of handwritten word recognizers on lexicons”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 24(12): 1553-1564 (2002)
  90. ↑ V. Govindaraju, P. Slavik*, and H. Xue, “Use of lexicon density in evaluating word recognizers”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 24(6): 789-800 (2002)
  91. ↑ H. Lei and V. Govindaraju, "A Comparative Study on the Consistency of Features in On-line Signature Verification", Pattern Recognition Letters, Elsevier Science, 26(15): 2483-2489 (2005)
  92. ↑ H. Xue, V. Govindaraju, “Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition”, IEEE Transactions Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 28(3): 458-462 (2006)
  93. ↑ S. Chikkerur, A. Cartwright, and V. Govindaraju, "Fingerprint Image Enhancement Using STFT Analysis", The Journal of Pattern Recognition, Elsevier Publishers, 40(1):198-211 (2007)
  94. ↑ S. Tulyakov, V. Govindaraju, "Use of identification trial statistics for the combination of biometric matchers", IEEE Transactions on Information Forensics and Security, IEEE Signal Processing Society Press, 3(4): 719-733 (2008)]
  95. ↑ H. Cao, A. Bhardwaj, V. Govindaraju, “A probabilistic method for keyword retrieval in handwritten document images”, Pattern Recognition Journal, Elsevier Press, 42(12): 3374-3382 (2009)
  96. ↑ S. Kompalli, S. Setlur, and V. Govindaraju, “Devanagari OCR using a recognition driven segmentation framework and stochastic language models”, International Journal of Document Analysis and Recognition, Springer Press, 11(2): 203-218 (2009)]
  97. ↑ H. Cao, V. Govindaraju, “Preprocessing of low quality handwritten documents using Markov random fields”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Society Press, 31(7): 1184-1194 (2009)
  98. ↑ S. Tulyakov, C. Wu, and V. Govindaraju, “On the difference between optimal combination functions for verification and identification systems”, International Journal Pattern Recognition and Artificial Intelligence, World Scientific Press, 24(2): 173-191 (2010)
  99. ↑ Y. Zhou, I. Nwogu, and V. Govindaraju, “Labeling Spain with Stanford”, IEEE Transactions on Image Processing, IEEE Signal Processing Society Press, 22(12): 5362-5371 (2013)
  100. ↑ M. Malgireddy, I. Nwogu, and V. Govindaraju, “Language motivated approach to action recognition”, Journal of Machine Learning Research, MIT Press, 14:2189−2212 (2013)

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