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Nalini K. Ratha
Researcher at IBM
Publications - 230
Citations - 13245
Nalini K. Ratha is an academic researcher from IBM. The author has contributed to research in topics: Biometrics & Fingerprint recognition. The author has an hindex of 50, co-authored 216 publications receiving 12290 citations. Previous affiliations of Nalini K. Ratha include Michigan State University & University at Buffalo.
Papers
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Proceedings ArticleDOI
Trustworthy AI
TL;DR: The tutorial on “Trustworthy AI” is proposed to address six critical issues in enhancing user and public trust in AI systems, namely: bias and fairness, explainability, robust mitigation of adversarial attacks, improved privacy and security in model building, and being decent.
Patent
Fingerprint biometric machine
TL;DR: In this article, an apparatus, method, and program storage device for representing biometrics is described, which includes a biometric feature extractor and a transformer, which is used to extract features corresponding to a given biometric depicted in an image.
Proceedings ArticleDOI
Improving classifier fusion via Pool Adjacent Violators normalization
TL;DR: This research explores an alternative method to combine classifiers at the score level and proposes the PAV algorithm for classifier fusion on publicly available NIST multi-modal biometrics score dataset, finding that it provides several advantages over existing techniques and is able to further improve the results obtained by other approaches.
Book ChapterDOI
Research Issues in Biometrics
TL;DR: The key research issues involved in the design of a biometric system are described to describe the key problems faced in the development of such a system.
Posted Content
Efficient CNN Building Blocks for Encrypted Data.
TL;DR: In this paper, the authors consider a Machine Learning as a Service (MLaaS) scenario where both input data and model parameters are secured using Fully Homomorphic Encryption (FHE) and show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters have a major impact on the design of the machine learning model.