arXiv subjects

Renu Sharma

Publications and source records attributed to Renu Sharma.

13 recordsLinked to original sources

arXiv2026

Beyond Mortality: Advancements in Post-Mortem Iris Recognition through Data Collection and Computer-Aided Forensic Examination

Post-mortem iris recognition brings both hope to the forensic community (a short-term but accurate and fast means of verifying identity) as well as concerns to society (its potential illicit use in post-mortem impersonation). These hopes and concerns have grown along with the volume of research in post-mortem iris recognition. Barriers to further progress in post-mortem iris recognition include the difficult nature of data collection, and the resulting small number of approaches designed specifically for comparing iris images of deceased subjects. This paper makes several unique contributions to mitigate these barriers. First, we have collected and we offer a new dataset of NIR (compliant with ISO/IEC 19794-6 where possible) and visible-light iris images collected after demise from 259 subjects, with the largest PMI (post-mortem interval) being 1,674 hours. For one subject, the data has been collected before and after death, the first such case ever published. Second, the collected dataset was combined with publicly-available post-mortem samples to assess the current state of the art in automatic forensic iris recognition with five iris recognition methods and data originating from 338 deceased subjects. These experiments include analyses of how selected demographic factors influence recognition performance. Thirdly, this study implements a model for detecting post-mortem iris images, which can be considered as presentation attacks. Finally, we offer an open-source forensic tool integrating three post-mortem iris recognition methods with explainability elements added to make the comparison process more human-interpretable.

cs.CV↗

arXiv2025

Task-conditioned Ensemble of Expert Models for Continuous Learning

One of the major challenges in machine learning is maintaining the accuracy of the deployed model (e.g., a classifier) in a non-stationary environment. The non-stationary environment results in distribution shifts and, consequently, a degradation in accuracy. Continuous learning of the deployed model with new data could be one remedy. However, the question arises as to how we should update the model with new training data so that it retains its accuracy on the old data while adapting to the new data. In this work, we propose a task-conditioned ensemble of models to maintain the performance of the existing model. The method involves an ensemble of expert models based on task membership information. The in-domain models-based on the local outlier concept (different from the expert models) provide task membership information dynamically at run-time to each probe sample. To evaluate the proposed method, we experiment with three setups: the first represents distribution shift between tasks (LivDet-Iris-2017), the second represents distribution shift both between and within tasks (LivDet-Iris-2020), and the third represents disjoint distribution between tasks (Split MNIST). The experiments highlight the benefits of the proposed method. The source code is available at https://github.com/iPRoBe-lab/Continuous_Learning_FE_DM.

cs.LG↗

arXiv2023

Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack Detection

Deep neural networks (DNNs) exhibit superior performance in various machine learning tasks, e.g., image classification, speech recognition, biometric recognition, object detection, etc. However, it is essential to analyze their sensitivity to parameter perturbations before deploying them in real-world applications. In this work, we assess the sensitivity of DNNs against perturbations to their weight and bias parameters. The sensitivity analysis involves three DNN architectures (VGG, ResNet, and DenseNet), three types of parameter perturbations (Gaussian noise, weight zeroing, and weight scaling), and two settings (entire network and layer-wise). We perform experiments in the context of iris presentation attack detection and evaluate on two publicly available datasets: LivDet-Iris-2017 and LivDet-Iris-2020. Based on the sensitivity analysis, we propose improved models simply by perturbing parameters of the network without undergoing training. We further combine these perturbed models at the score-level and at the parameter-level to improve the performance over the original model. The ensemble at the parameter-level shows an average improvement of 43.58% on the LivDet-Iris-2017 dataset and 9.25% on the LivDet-Iris-2020 dataset. The source code is available at https://github.com/redwankarimsony/WeightPerturbation-MSU.

cs.CV↗

arXiv2023

Iris Presentation Attack: Assessing the Impact of Combining Vanadium Dioxide Films with Artificial Eyes

Iris recognition systems, operating in the near infrared spectrum (NIR), have demonstrated vulnerability to presentation attacks, where an adversary uses artifacts such as cosmetic contact lenses, artificial eyes or printed iris images in order to circumvent the system. At the same time, a number of effective presentation attack detection (PAD) methods have been developed. These methods have demonstrated success in detecting artificial eyes (e.g., fake Van Dyke eyes) as presentation attacks. In this work, we seek to alter the optical characteristics of artificial eyes by affixing Vanadium Dioxide (VO2) films on their surface in various spatial configurations. VO2 films can be used to selectively transmit NIR light and can, therefore, be used to regulate the amount of NIR light from the object that is captured by the iris sensor. We study the impact of such images produced by the sensor on two state-of-the-art iris PA detection methods. We observe that the addition of VO2 films on the surface of artificial eyes can cause the PA detection methods to misclassify them as bonafide eyes in some cases. This represents a vulnerability that must be systematically analyzed and effectively addressed.

cs.CV↗

arXiv2022

Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey

The performance of face recognition systems can be negatively impacted in the presence of masks and other types of facial coverings that have become prevalent due to the COVID-19 pandemic. In such cases, the periocular region of the human face becomes an important biometric cue. In this article, we present a detailed review of periocular biometrics. We first examine the various face and periocular techniques specially designed to recognize humans wearing a face mask. Then, we review different aspects of periocular biometrics: (a) the anatomical cues present in the periocular region useful for recognition, (b) the various feature extraction and matching techniques developed, (c) recognition across different spectra, (d) fusion with other biometric modalities (face or iris), (e) recognition on mobile devices, (f) its usefulness in other applications, (g) periocular datasets, and (h) competitions organized for evaluating the efficacy of this biometric modality. Finally, we discuss various challenges and future directions in the field of periocular biometrics.

cs.CV↗

arXiv2020

Viability of Optical Coherence Tomography for Iris Presentation Attack Detection

In this paper, we propose the use of Optical Coherence Tomography (OCT) imaging for the problem of iris presentation attack (PA) detection. We assess its viability by comparing its performance with respect to traditional iris imaging modalities, viz., near-infrared (NIR) and visible spectrum. OCT imaging provides a cross-sectional view of an eye, whereas traditional imaging provides 2D iris textural information. PA detection is performed using three state-of-the-art deep architectures (VGG19, ResNet50 and DenseNet121) to differentiate between bonafide and PA samples for each of the three imaging modalities. Experiments are performed on a dataset of 2,169 bonafide, 177 Van Dyke eyes and 360 cosmetic contact images acquired using all three imaging modalities under intra-attack (known PAs) and cross-attack (unknown PAs) scenarios. We observe promising results demonstrating OCT as a viable solution for iris presentation attack detection.

cs.CV↗

arXiv2020

Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition

Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series: LivDet-Iris 2020. This year's competition introduced several novel elements: (a) incorporated new types of attacks (samples displayed on a screen, cadaver eyes and prosthetic eyes), (b) initiated LivDet-Iris as an on-going effort, with a testing protocol available now to everyone via the Biometrics Evaluation and Testing (BEAT)(https://www.idiap.ch/software/beat/) open-source platform to facilitate reproducibility and benchmarking of new algorithms continuously, and (c) performance comparison of the submitted entries with three baseline methods (offered by the University of Notre Dame and Michigan State University), and three open-source iris PAD methods available in the public domain. The best performing entry to the competition reported a weighted average APCER of 59.10\% and a BPCER of 0.46\% over all five attack types. This paper serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments.

cs.CV↗

arXiv2020

D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector

An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes, or cosmetic contact lenses to circumvent the system. In this work, we propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture. It demonstrates generalizability across PA artifacts, sensors and datasets. Experiments conducted on a proprietary dataset and a publicly available dataset (LivDet-2017) substantiate the effectiveness of the proposed method for iris PA detection. The proposed method results in a true detection rate of 98.58\% at a false detection rate of 0.2\% on the proprietary dataset and outperfoms state-of-the-art methods on the LivDet-2017 dataset. We visualize intermediate feature distributions and fixation heatmaps using t-SNE plots and Grad-CAM, respectively, in order to explain the performance of D-NetPAD. Further, we conduct a frequency analysis to explain the nature of features being extracted by the network. The source code and trained model are available at https://github.com/iPRoBe-lab/D-NetPAD.

cs.CV↗

arXiv2019

Some Research Problems in Biometrics: The Future Beckons

The need for reliably determining the identity of a person is critical in a number of different domains ranging from personal smartphones to border security; from autonomous vehicles to e-voting; from tracking child vaccinations to preventing human trafficking; from crime scene investigation to personalization of customer service. Biometrics, which entails the use of biological attributes such as face, fingerprints and voice for recognizing a person, is being increasingly used in several such applications. While biometric technology has made rapid strides over the past decade, there are several fundamental issues that are yet to be satisfactorily resolved. In this article, we will discuss some of these issues and enumerate some of the exciting challenges in this field.

cs.CV↗

arXiv2017

Metastable morphological states of catalytic nanoparticles

During the catalytic synthesis of graphene, nanotubes, fibers, and other nanostructures, many intriguing phenomena occur, such as phase separation, precipitation, and analogs of capillary action. We demonstrate that catalytic nanoparticles display metastable states that influence growth, reminiscent of some protein ensembles in vivo. As a carbon nanostructure grows, the nanoparticle elongates due to an energetically favorable metal-carbon interaction that overrides the surface energy increase of the metal. The formation of subsequent nested tubes, however, drives up the particle's free energy, but the particle remains trapped until an accessible free energy surface allows it to exit the tube. During this time, the nanoparticle continues to catalyze tube growth internally within the nested structure. This nonequilibrium thermodynamic cycle of elongation and retraction is heavily influenced by tapering of the structure, which, ultimately, determines the final product and catalyst lifetime. Our results provide a unifying framework to interpret similar phenomena for other catalytic reactions, such as during CO oxidation, and suggest routes to the practical optimization of such processes.

cond-mat.mes-hall↗

arXiv2015

Creating Nanostructured Superconductors On Demand by Local Current Annealing

Superconductivity results from a Bose condensate of Cooper-paired electrons with a macroscopic quantum wavefunction. Dramatic effects can occur when the region of the condensate is shaped and confined to the nanometer scale. Recent progress in nanostructured superconductors has revealed a route to topological superconductivity, with possible applications in quantum computing. However, challenges remain in controlling the shape and size of specific superconducting materials. Here, we report a new method to create nanostructured superconductors by partial crystallization of the half-Heusler material, YPtBi. Superconducting islands, with diameters in the range of 100 nm, were reproducibly created by local current annealing of disordered YPtBi in the tunneling junction of a scanning tunneling microscope (STM). We characterize the superconducting island properties by scanning tunneling spectroscopic measurements to determine the gap energy, critical temperature and field, coherence length, and vortex formations. These results show unique properties of a confined superconductor and demonstrate that this new method holds promise to create tailored superconductors for a wide variety of nanometer scale applications.

cond-mat.mes-hall↗

arXiv2015

Probing gas adsorption on individual facets of a metal nanoparticle

Metal nanoparticle surfaces comprise of multiple planes with various atomic arrangements that interact with gases differently1,2. Identification of gas adsorption properties on all facets is an essential prerequisite for rational design of metal nanoparticles for catalysis, energy storage and gas sensing. Adsorbed gas molecules alter the electron density at metal surfaces3, changing the energy of the surface plasmon resonance4,5. All-optical methods using light as the excitation source can identify, in situ, gas-metal interactions in an ensemble of metal nanoparticles by measuring energy shifts of either stationary surface plasmon resonances over an entire particle, or delocalized symmetric modes on multiple regions in a particle6-8. Such methods preclude the characterization of facet-dependent gas adsorption for individual nanoparticles. Here, by using in situ electron-energy-loss spectroscopy in an environmental scanning-transmission electron microscope, we show that localized, stationary surface plasmons on individual facets of triangular crystalline Au nanoparticles in vacuum and in gaseous environments can be excited using a nanometer size electron probe. We then show that, by exploiting this localized spatial resolution, selective gas adsorption on specific sets of facets can be characterized. We anticipate that this method can be extended to quantify the concentration of adsorbed molecules, derive the binding energy for specific facets for certain gas species, and design facet-controlled nanoparticles to achieve specific gas adsorption properties.

cond-mat.mtrl-sci↗

arXiv2014

Insights into carbon nanotube nucleation: Cap formation governed by catalyst interfacial step flow

In order to accommodate an increasing demand for carbon nanotubes (CNTs) with desirable characteristics one has to understand the origin of helicity of their structures. Here, through in situ microscopy we demonstrate that the nucleation of a carbon nanotube is initiated by the formation of the carbon cap. Nucleation begins with the formation of a graphene embryo that is bound between opposite step-edges on the nickel catalyst surface. The embryo grows larger as the step-edges migrate along the surface, leading to the formation of a curved carbon cap when the steps flow across the edges of adjacent facets. Further motion of the steps away from the catalyst tip with attached rims of the carbon cap generates the wall of the nanotube. Density Functional Theory calculations bring further insight into the process, showing that step flow occurs by surface self diffusion of the nickel atoms via a step-edge attachment-detachment mechanism. Since the fact that cap forms first in the sequence of stages involved in nanotube growth, we suggest that it originates the helicity of the nanotube. Therefore, the angular distribution of catalyst facets could be exploited as a new parameter for controlling the curvature of the cap and, presumably, the helicity of the nanotube.

cond-mat.mtrl-sci↗