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Anil Jain - Michigan State University. East Lansing, MI, US

Anil Jain

University Distinguished Professor, Department of Computer Science | Michigan State University


Expert in biometrics (pattern recognition, computer vision and biometric recognition)







APSIPA Interview: Prof. Anil K. Jain (Michigan State University) Anil Jain: 25 Years of Biometric Recognition Biometrics - Technology for Human Recognition - Presented by Anil K. Jain, Ph.D.



Anil K. Jain is a University Distinguished Professor in the Department of Computer Science & Engineering at Michigan State University. He was appointed an Honorary Professor at Tsinghua University and a WCU Distinguished Adjunct Professor at Korea University. He received B.Tech degree from the Indian Institute of Technology, Kanpur and M.S. and Ph.D. degrees from The Ohio State University. His research interests include pattern recognition, computer vision and biometric recognition.

In 2020, he ranked No. 1 in Guide2Research's 2020 edition of Top Scientists Ranking for Computer Science and Electronics. He has been recognized with a Guggenheim Fellowship, Humboldt Research Award, Fulbright fellowship, IEEE Computer Society Technical Achievement award, IEEE W. Wallace McDowell award, IAPR King-Sun Fu Prize, IEEE ICDM Research Contribution award, IAPR Senior Biometric Investigator award, MSU Withrow Teaching Excellence award, and the MSU 2014 Innovator of the Year award. He served as the Editor-in-Chief of the IEEE Trans. Pattern Analysis and Machine Intelligence (1991-1994) and is a Fellow of the ACM, IEEE, AAAS, IAPR and SPIE.

Anil Jain has been assigned six U.S. patents on fingerprint recognition (transferred to IBM in 1999) and two Korean patents on video surveillance. He has also licensed technologies of particular interest to forensics and law enforcement agencies to Safran Morpho and NEC Corp: (i) Tattoo-ID for matching tattoo images (2012), (ii) AltFinger-ID for detecting whether a fingerprint image has been altered (2013), (iii) FaceSketch-ID for matching facial sketches to mugshot images (2014), and (iv) Face-Search for locating a person of interest in databases with hundreds of millions of faces (2015).

He is the author of several popular books, including Introduction to Biometrics (2011), Handbook of Face Recognition (first edition: 2005; second edition 2011), Handbook of Fingerprint Recognition (first edition: 2003, second edition: 2009), Markov Random Fields: Theory and Applications (1993), and Algorithms For Clustering Data (1988). His list of publications is available at Google Scholar

Industry Expertise (8)

Computer Hardware

Computer Networking

Computer Software




Writing and Editing

Electrical Engineering

Areas of Expertise (5)

Pattern Recognition

Computer Vision


Markov Random Fields

Clustering Data

Accomplishments (2)

Withrow Teaching Excellence Award (professional)


Awarded by Michigan State University

• King-Sun Fu Prize (professional)


Awarded by the International Association of Pattern Recognition

Education (3)

Ohio State University: Ph.D., Electrical Engineering 1973

Ohio State University: M.S., Electrical Engineering 1970

Indian Institute of Technology, Kanpur: B.Tech., Electrical Engineering 1969

Affiliations (4)

  • National Academy of Engineering
  • Indian National Academy of Engineering
  • AAAS Latent fingerprint Working Group
  • National Academy of Inventors: Fellow

News (2)

Identifying Children and Saving Lives One Thumbprint at a Time

MSU Today  online


Jain and his team of biometrics researchers demonstrated in a first-of-its-kind study that digital scans of a young child’s fingerprint can be correctly recognized one year later. In particular, the team showed they can correctly identify children 6 months old over 99 percent of the time based on their two thumbprints. A child could then be identified at each medical visit by a simple fingerprint scan, allowing them to get proper medical care such as life-saving immunizations or food supplements...

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Creating 3-D Hands to Keep Us Safe and Increase Security

MSU Today  online


Jain and his biometrics team were studying how to test and calibrate fingerprint scanners commonly used across the globe at police departments, airport immigration counters, banks and even amusement parks. Without a standard life-like 3-D model to test the scanners with, there’s no consistent and repeatable way to determine the accuracy of the scans and establish which scanner is better...

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Journal Articles (5)

LemurFaceID: a face recognition system to facilitate individual identification of lemurs

BMC Zoology

2016 Long-term research of known individuals is critical for understanding the demographic and evolutionary processes that influence natural populations. Current methods for individual identification of many animals include capture and tagging techniques and/or researcher knowledge of natural variation in individual phenotypes. These methods can be costly, time-consuming, and may be impractical for larger-scale, population-level studies. Accordingly, for many animal lineages, long-term research projects are often limited to only a few taxa. Lemurs, a mammalian lineage endemic to Madagascar, are no exception. Long-term data needed to address evolutionary questions are lacking for many species. This is, at least in part, due to difficulties collecting consistent data on known individuals over long periods of time. Here, we present a new method for individual identification of lemurs (LemurFaceID). LemurFaceID is a computer-assisted facial recognition system that can be used to identify individual lemurs based on photographs.

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Fingerprint recognition of young children

IEEE Transactions on Information Forensics and Security

2016 In 1899, Galton first captured ink-on-paper fingerprints of a single child from birth until the age of 4.5 years, manually compared the prints, and concluded that “the print of a child at the age of 2.5 years would serve to identify him ever after”. Since then, ink-on-paper fingerprinting and manual comparison methods have been superseded by digital capture and automatic fingerprint comparison techniques, but only a few feasibility studies on child fingerprint recognition have been conducted. Here, we present the first systematic and rigorous longitudinal study that addresses the following questions: (i) Do fingerprints of young children possess the salient features required to uniquely recognize a child? (ii) If so, at what age can a child’s fingerprints be captured with sufficient fidelity for recognition? (iii) Can a child’s fingerprints be used to reliably recognize the child as he ages? For our study, we collected fingerprints of 309 children (0-5 years old) four different times over a one year period. We show, for the first time, that fingerprints acquired from a child as young as 6 hours old exhibit distinguishing features necessary for recognition, and that state-of-the-art fingerprint technology achieves high recognition accuracy (98.9% true accept rate at 0.1% false accept rate) for children older than 6 months. Additionally, we use mixed-effects statistical models to study the persistence of child fingerprint recognition accuracy and show that the recognition accuracy is not significantly affected over the one year time lapse in our data. Given rapidly growing requirements to recognize children for vaccination tracking, delivery of supplementary food, and national identification documents, our study demonstrates that fingerprint recognition of young children (6 months and older) is a viable solution based on available capture and recognition technology.

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Adaptive fusion of biometric and biographic information for identity de-duplication

Pattern Recognition Letters

2016 Use of biometrics for person identification has increased tremendously over the past decade, e.g., in large scale national identification programs, for law enforcement and border control applications, and social welfare initiatives. For such large scale applications with a diverse target population, unimodal biometric systems, which use a single biometric trait (e.g., fingerprints), are inadequate due to their limited capacity. Multimodal biometric systems, which fuse multiple biometric traits (e.g., fingerprints and face), are required for large-scale identification applications, e.g., de-duplication where the goal is to ensure that the same person does not have two different official credentials (e.g., national ID card) based on different credentials. While multimodal biometric systems offer several advantages (e.g., improvement in recognition accuracy, decrease in failure to enroll rate), they require large enrollment and de-duplication times. This paper proposes an adaptive sequential framework to automatically determine which subset of biometric traits and biographic information is adequate for de-duplication of a given query. An analysis of this strategy is presented on a virtual multi-biometric database of 27,000 subjects (fingerprints from NIST SD14 dataset and face images from the PCSO dataset) along with biographic information sampled from the US census data. Experimental results, using three-fold cross-validation, show that without any loss in de-duplication accuracy, on average, for 63.18% (of a total of 27,000) of the queries, only fingerprint capture is adequate, for an additional 28.69% of queries, both fingerprint and face are required, and only 8.13% of the queries needed biographic information in addition to fingerprint and face.

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50 years of biometric research: Accomplishments, challenges, and opportunities

Pattern Recognition Letters

2016 Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Mitchell Trauring in the journal Nature in 1963. The first objective of this paper is to document the significant progress that has been achieved in the field of biometric recognition in the past 50 years since Trauring's landmark paper. This progress has enabled current state-of-the-art ...

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A fast and accurate unconstrained face detector

IEEE Transactions on Pattern Analysis and Machine Intelligence

2016 We propose a method to address challenges in unconstrained face detection, such as arbitrary pose variations and occlusions. First, a new image feature called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed as the difference to sum ratio between two pixel values, inspired by the Weber Fraction in experimental psychology. The new feature is scale invariant, bounded, and is able to reconstruct the original image. Second, we propose a deep quadratic tree to learn the optimal subset of NPD features ...

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