Anil Jain

University Distinguished Professor, Department of Computer Science Michigan State University

  • East Lansing MI

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

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2 min

Permanent records – learn how a MSU expert is using thumbprints, vaccination records to save children’s lives

As COVID-19 vaccination efforts ramp up in developing nations, accurate and accessible vaccination records are critical for children who often lack official identification necessary for the delivery of government or medical assistance. Fortunately, the solution to this problem and the broader need for child identification has been researched for years now by Anil Jain, a Michigan State University Distinguished Professor of computer science and engineering. Jain and his team of researchers — Joshua Engelsma, Debayan Deb and Kai Cao — are now advancing the use of groundbreaking fingerprint recognition systems for children as young as a few months old. “The research of Jain and his team is unique in its rigor and in the promise that it embodies,” said Joseph Atick, executive chairman of ID4Africa. “Solving the infant ID problem through fingerprints will have profound consequences to the development agenda as a whole and to civil registration, child protection and health management, in particular. It will give today's invisible children in the developing world a legal identity by tracing them to their origin, enabling them to assert their rights and to be fully included in society.” Starting in 2014, Jain and his team began developing fingerprint recognition that worked well for toddlers,1 year and older. The researchers traveled to Saran Ashram hospital in Dayalbagh, India, where, over a one-year time span, they fingerprinted the same children multiple times to show they could be reliably recognized based only upon their fingerprints. The team's latest breakthrough employs an $80, high-resolution (1900ppi) infant fingerprint reader. It developed along with a high-resolution fingerprint matcher. Prints taken in children as young as two months were still recognizable a year later. It’s a compelling story, and the complete article is attached here. Despite efforts of international health organizations and NGOs (nongovernmental organizations), children are still dying because it’s been believed that it wasn’t possible to use body traits such as fingerprints to identify children. We’ve just demonstrated that it is indeed possible,” Jain said. In many developing countries, identification documents are kept as paper records, but paper is easily lost, destroyed, forged or stolen. Fingerprints are purportedly unique and, once captured in a database, could be accessed by medical professionals to reliably record immunization schedules and other medical information. In additional to accessing medical records, capturing a child’s fingerprint has several additional uses such as civil registries, lifetime identities and improving nutrition. This is an amazing feat and one worth covering. If you are journalist looking to learn more – then let us help. Anil K. Jain is a University Distinguished Professor in the Department of Computer Science & Engineering at Michigan State University and is a renowned expert in biometrics. Jain is available to speak to media about facial recognition technology simply click on his icon to arrange an interview today.

Anil Jain

1 min

Facial recognition was used to quickly catch the Maryland newspaper shooting suspect - Our experts can explain how.

At Michigan State University's Pattern Recognition and Image Processing laboratory, Anil Jain has demonstrated many times that under controlled conditions, when the face is angled toward the camera and if the lighting is good, this technology can be up to 99 percent accurate. Jain is available to speak to media about facial recognition technology simply click on his icon to arrange an interview today. Source:

Anil Jain

Media

Biography

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

Computer Hardware
Computer Networking
Computer Software
Biotechnology
Research
Education/Learning
Writing and Editing
Electrical Engineering

Areas of Expertise

Pattern Recognition
Computer Vision
Biometrics
Markov Random Fields
Clustering Data

Accomplishments

Withrow Teaching Excellence Award

2014-01-01

Awarded by Michigan State University

• King-Sun Fu Prize

2008-01-01

Awarded by the International Association of Pattern Recognition

Education

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

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

News

Identifying Children and Saving Lives One Thumbprint at a Time

MSU Today  online

2016-09-21

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

2016-10-18

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

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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