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.

On Deep Learning for Dorsal Hand Vein Recognition

Abstract: The use of biometrics has been one of the most effective solutions for a person’s identification and verification. Traditional biometric modalities such as fingerprint, iris, and face recognition have been successfully employed and have shown tremendous success in providing a secure access mechanism. On top of that, the success of deep learning algorithms has showcased that automated biometrics recognition has the potential of surpassing human-level accuracy. Another relatively unexplored biometric modality namely Dorsal Hand Vein (DHV) recently has gained traction in the industry and among researchers from academia. In this paper, we have designed an end-to-end pipeline for DHV biometric authentication that includes image enhancement, region of interest (ROI) extraction, and finally deep learning models for DHV recognition. Three deep learning models namely a custom convolutional neural network (CNN), a Siamese network, and a Triplet Network are trained on publicly available images of DHV datasets. Later, these models are used as feature extractors and tested on images of unseen subjects for authentication. We find that the simple CNN model learns a better feature representation than the Triplet network, which outperforms the Siamese network. One potential reason for such behavior is the limited availability of the datasets used in training.

Airborne anxiety detection and remediation

Abstract: Embodiments include methods, systems, and computer program products for detection and remediation of anxiety. Aspects include receiving an anxiety indicator. Aspects also include analyzing the anxiety indicator to determine whether an anxiety level exceeds an anxiety threshold. Aspects also include, based upon a determination that the anxiety level exceeds the anxiety threshold, selecting a first sound. Aspects also include outputting the first sound. Aspects also include receiving an anxiety feedback. Aspects also include determining, based upon the anxiety feedback, whether the anxiety level is decreasing.

Cohort based adversarial attack detection

Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.

Cognitive personal health limit detector and trainer using a wearable smart mobile device

Abstract: A computer-implemented method, data processor and computer program product determine exposure levels to external stimuli. At least one environmental condition is monitored and an external stimulus event is identified based on the at least one environmental condition. An intensity level of the external stimulus event is determined to exceed a predetermined threshold. An exposure level of the external stimulus is determined by integrating the intensity level over time. At least one human biometric quantity of a user is measured and a personal exposure level limitation is determined for the user based on the measured at least one human biometric quantity. When the exposure level exceeds the personal exposure level limitation, the user is warned of the exposure level. The human biometric quantity is one of: heart rate, blood pressure, body temperature, glucose level, blood oxygen level, muscle activity, electrolyte level, and lactic acid level.

Data clustering: a review

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.

Handbook of Fingerprint Recognition

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

Fingerprint image enhancement: algorithm and performance evaluation

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.

[서평]「Applied Cryptography」

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.