scispace - formally typeset
Search or ask a question
Author

Nalini K. Ratha

Bio: 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
More filters
Journal ArticleDOI
TL;DR: This work proposes the use of fingerprint video sequences to investigate detecting two aspects of the dynamic behavior of fingerprints, and describes a new concept called the "resultant biometrics", a new type of biometric which has both a physiological, physical, and temporal component, added by a subject to an existing biometric.
Abstract: Traditional fingerprint acquisition is limited to single-image capture and processing. With the advent of faster capture hardware, faster processors, and advances in video compression standards, newer systems can capture and exploit video signals for tasks that are difficult using a single image. We propose the use of fingerprint video sequences to investigate detecting two aspects of the dynamic behavior of fingerprints. Specifically, we are interested in the detection of distortion of fingerprint impressions due to excessive force and the detection of the positioning of fingers during image capture. These issues often lead to difficulties in establishing a precise match between acquired images. The proposed techniques investigate dynamic characteristics of fingerprints across video sequence frames. A significant advantage of our approach for distortion analysis is that it works directly on MPEG-1,-2 encoded fingerprint video bitstreams. The proposed methods have been tested on the NIST-24 live-scan fingerprint video database and the results are promising. We also describe a new concept called the "resultant biometrics", a new type of biometrics which has both a physiological, physical (e.g., force, torque, linear motion, rotation) component and/or a temporal characteristic, added by a subject to an existing biometric. This resultant biometric is both desirable and efficient in terms of easy modification of compromised biometrics and is harder to produce with spoof body parts.

22 citations

Proceedings ArticleDOI
Nalini K. Ratha1, Anil K. Jain
20 Oct 1997
TL;DR: This paper describes the usage of custom computing approach to meet the computation and communication needs of computer vision algorithms and demonstrates the advantages of this approach using Splash 2-a Xilinx 4010-based custom computer.
Abstract: Algorithms in computer vision are characterized by (i) complex and repetitive operations; (ii) large amount of data and (iii) a variety of data interaction (e.g., point operations, neighborhood operations, global operations). Based on the computation and communication complexity, vision algorithms have been characterized into three categories: (i) low-level, (ii) intermediate-level and (iii) high-level. In this paper, we describe the usage of custom computing approach to meet the computation and communication needs of computer vision algorithms. By customizing hardware architecture for every application at the instruction level, the optimal grain size needed for the problem at hand and the instruction granularity can be matched. Field Programmable Gate Array (FPGA) based processing elements (PEs) are being used to provide this facility. Using programmable communication resources, the diverse communication requirements can be met. A vision system needs to integrate hardware for the three levels. A custom computing approach alleviates the problem of achieving optimal granularity for different stages as the same hardware gets reconfigured at a software level for different levels of the application. We demonstrate the advantages of our approach using Splash 2-a Xilinx 4010-based custom computer.

22 citations

Proceedings ArticleDOI
14 Jun 2020
TL;DR: A novel "defense layer" in a network which aims to block the generation of adversarial noise and prevents an adversarial attack in black-box and gray-box settings is presented.
Abstract: Several successful adversarial attacks have demonstrated the vulnerabilities of deep learning algorithms. These attacks are detrimental in building deep learning based dependable AI applications. Therefore, it is imperative to build a defense mechanism to protect the integrity of deep learning models. In this paper, we present a novel "defense layer" in a network which aims to block the generation of adversarial noise and prevents an adversarial attack in black-box and gray-box settings. The parameter-free defense layer, when applied to any convolutional network, helps in achieving protection against attacks such as FGSM, L 2 , Elastic-Net, and DeepFool. Experiments are performed with different CNN architectures, including VGG, ResNet, and DenseNet, on three databases, namely, MNIST, CIFAR-10, and PaSC. The results showcase the efficacy of the proposed defense layer without adding any computational overhead. For example, on the CIFAR-10 database, while the attack can reduce the accuracy of the ResNet-50 model to as low as 6.3%, the proposed "defense layer" retains the original accuracy of 81.32%.

21 citations

Book ChapterDOI
Nalini K. Ratha1
04 Jan 2010
TL;DR: A pattern recognition-based model is presented to analyze the threats to a biometrics-based authentication system and also a novel solution to enhances privacy.
Abstract: While biometrics is useful for secure and accurate identification of a person, it also has serious privacy implications in handling large databases of biometrics Standard encryption techniques have limited use for handling biometrics templates and signals as intra-person variations can't be handled effectively using information security techniques As a way to enhance privacy and security of biometrics databases, we present a pattern recognition-based model to analyze the threats to a biometrics-based authentication system and also a novel solution to enhances privacy Cancelable biometrics is an emerging concept, where transformations that hide the biometrics signatures (fingerprints, faces and iris) are designed so that the identity can be established with added security Other methods have been proposed for enhancing privacy in biometrics In this paper, we will describe our threat model for biometrics recognition and recent advances proposed for privacy enhancements for fingerprint and iris biometrics

21 citations

Proceedings ArticleDOI
08 Dec 2008
TL;DR: This paper proposes a novel gradient-based approach to characterize textural information in fingerprints for the task of biometric matching that uses histograms of oriented gradients to represent minutiae neighborhoods.
Abstract: Though a lot of research has been done to match fingerprints, most existing approaches rely on locations of minutiae features for matching tasks. Relatively, little effort has gone into utilizing textural information present in fingerprints as distinguishing characteristic. In this paper, we propose a novel gradient-based approach to characterize textural information in fingerprints for the task of biometric matching. In particular, the proposed approach uses histograms of oriented gradients (HOGs) to represent minutiae neighborhoods. The minutiae neighborhoods are divided into several regions to make the computed histograms distinguishing and robust at the same time. Experimental results are provided to show the efficacy of the proposed characterization.

20 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

14,054 citations

Book
10 Mar 2005
TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
Abstract: A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

3,821 citations

Journal ArticleDOI
TL;DR: A fast fingerprint enhancement algorithm is presented, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency.
Abstract: In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

2,212 citations

01 Apr 1997
TL;DR: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity.
Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

2,188 citations

Reference EntryDOI
15 Oct 2004

2,118 citations