Search Results for author:
Found 59 papers, 17 papers with code
Privacy-Safe Iris Presentation Attack Detection
This paper proposes a framework for a privacy-safe iris presentation attack detection (PAD) method, designed solely with synthetically-generated, identity-leakage-free iris images.
Grains of Saliency: Optimizing Saliency-based Training of Biometric Attack Detection Models
Incorporating human-perceptual intelligence into model training has shown to increase the generalization capability of models in several difficult biometric tasks, such as presentation attack detection (PAD) and detection of synthetic samples.
SiNC+: Adaptive Camera-Based Vitals with Unsupervised Learning of Periodic Signals
We present the first non-contrastive unsupervised learning framework for signal regression to mitigate the need for labelled video data.
Forensic Iris Image-Based Post-Mortem Interval Estimation
To assess the feasibility of the iris-based PMI estimation, convolutional neural networks-based models (VGG19, DenseNet121, ResNet152, and Inception_v3) were trained to predict the PMI from (a) near-infrared (NIR), (b) visible (RGB), and (c) multispectral forensic iris images.
MSPM: A Multi-Site Physiological Monitoring Dataset for Remote Pulse, Respiration, and Blood Pressure Estimation
We provide thorough experiments demonstrating the suitability of MSPM to support research on rPPG, respiration rate, and PTT.
EyePreserve: Identity-Preserving Iris Synthesis
Synthesis of same-identity biometric iris images, both for existing and non-existing identities while preserving the identity across a wide range of pupil sizes, is complex due to intricate iris muscle constriction mechanism, requiring a precise model of iris non-linear texture deformations to be embedded into the synthesis pipeline.
Forensic Iris Image Synthesis
This paper makes a novel contribution to facilitate progress in post-mortem iris recognition by offering a conditional StyleGAN-based iris synthesis model, trained on the largest-available dataset of post-mortem iris samples acquired from more than 350 subjects, generating -- through appropriate exploration of StyleGAN latent space -- multiple within-class (same identity) and between-class (different new identities) post-mortem iris images, compliant with ISO/IEC 29794-6, and with decomposition deformations controlled by the requested PMI (post mortem interval).
MENTOR: Human Perception-Guided Pretraining for Increased Generalization
In this paper, we introduce MENTOR (huMan pErceptioN-guided preTraining fOr increased geneRalization), which addresses this question through two unique rounds of training the CNNs tasked with open-set anomaly detection.
Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition
New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark.
Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training
We compare the accuracy achieved by our teacher-student training paradigm with (1) training using all available human salience annotations, and (2) using all available training data without human salience annotations.
Non-Generative Energy Based Models
Energy-based models (EBM) have become increasingly popular within computer vision.
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models
In this paper, we propose five novel methods of leveraging model salience to explain a model behavior at scale.
Full-Body Cardiovascular Sensing with Remote Photoplethysmography
In this paper we explored the feasibility of rPPG from non-face body regions such as the arms, legs, and hands.
Non-Contrastive Unsupervised Learning of Physiological Signals from Video
Given the limited inductive biases and impressive empirical results, the approach is theoretically capable of discovering other periodic signals from video, enabling multiple physiological measurements without the need for ground truth signals.
Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimation
Extensive experimentation with eight research datasets (rPPG-specific: DDPM, CDDPM, PURE, UBFC, ARPM; deep fakes: DFDC; face presentation attack detection: HKBU-MARs; rPPG outlier: KITTI) show better accuracy of anomaly detection for deep learning models incorporating the proposed training (75. 8%), compared to models trained regularly (73. 7%) and to hand-crafted rPPG methods (52-62%).
Improving Model's Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors
Deep learning-based models generalize better to unknown data samples after being guided "where to look" by incorporating human perception into training strategies.
Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises
Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of objects, such as faces, animals, and automobiles.
State Of The Art In Open-Set Iris Presentation Attack Detection
We have curated the largest publicly-available image dataset for this problem, drawing from 26 benchmarks previously released by various groups, and adding 150, 000 images being released with the journal version of this paper, to create a set of 450, 000 images representing authentic iris and seven types of presentation attack instrument (PAI).
The Value of AI Guidance in Human Examination of Synthetically-Generated Faces
Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones.
Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition
Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons.
DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition.
Saliency-Guided Textured Contact Lens-Aware Iris Recognition
Iris recognition requires an adequate level of the iris texture being visible to perform a reliable matching.
Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition
In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts.
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning
This new approach incorporates human-annotated saliency maps into a loss function that guides the model's learning to focus on image regions that humans deem salient for the task.
Digital and Physical-World Attacks on Remote Pulse Detection
Remote photoplethysmography (rPPG) is a technique for estimating blood volume changes from reflected light without the need for a contact sensor.
Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results
Our application context is an interview scenario in which the interviewee attempts to deceive the interviewer on selected responses.
Human-Aided Saliency Maps Improve Generalization of Deep Learning
One ongoing challenge is how to achieve the greatest accuracy in cases where training data is limited.
Remote Pulse Estimation in the Presence of Face Masks
Remote photoplethysmography (rPPG), a family of techniques for monitoring blood volume changes, may be especially useful for widespread contactless health monitoring using face video from consumer-grade visible-light cameras.
This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models
This raises privacy-related questions, but also stimulates discussions of (a) the face manifold's characteristics in the feature space and (b) how to create generative models that do not inadvertently reveal identity information of real subjects whose images were used for training.
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).
Open Source Iris Recognition Hardware and Software with Presentation Attack Detection
This paper proposes the first known to us open source hardware and software iris recognition system with presentation attack detection (PAD), which can be easily assembled for about 75 USD using Raspberry Pi board and a few peripherals.
Iris Presentation Attack Detection: Where Are We Now?
As the popularity of iris recognition systems increases, the importance of effective security measures against presentation attacks becomes paramount.
Robust Iris Presentation Attack Detection Fusing 2D and 3D Information
Diversity and unpredictability of artifacts potentially presented to an iris sensor calls for presentation attack detection methods that are agnostic to specificity of presentation attack instruments.
Are Gabor Kernels Optimal for Iris Recognition?
We use (on purpose) a single-layer convolutional neural network as it mimics an iris code-based algorithm.
Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?
Features are extracted from each convolutional layer and the classification accuracy achieved by a Support Vector Machine is measured on a dataset that is disjoint from the samples used in training of the ResNet-50 model.
Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes
This paper proposes an end-to-end iris recognition method designed specifically for post-mortem samples, and thus serving as a perfect application for iris biometrics in forensics.
Post-mortem Iris Decomposition and its Dynamics in Morgue Conditions
With increasing interest in employing iris biometrics as a forensic tool for identification by investigation authorities, there is a need for a thorough examination and understanding of post-mortem decomposition processes that take place within the human eyeball, especially the iris.
Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation
We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images.
CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation
Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations.
Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples
Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation.
Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks
This paper offers three new, open-source, deep learning-based iris segmentation methods, and the methodology how to use irregular segmentation masks in a conventional Gabor-wavelet-based iris recognition.
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts).
Cross-Domain Iris Presentation Attack Detection
Iris Recognition
+1
Iris Presentation Attack Detection Based on Photometric Stereo Features
2, 900 iris image pairs acquired from approx.
Binary Classification
Cross-Domain Iris Presentation Attack Detection
Open Source Presentation Attack Detection Baseline for Iris Recognition
This paper proposes the first, known to us, open source presentation attack detection (PAD) solution to distinguish between authentic iris images (possibly wearing clear contact lenses) and irises with textured contact lenses.
Iris recognition in cases of eye pathology
To make this study possible, a special database of iris images has been used, representing more than 20 different medical conditions of the ocular region (including cataract, glaucoma, rubeosis iridis, synechiae, iris defects, corneal pathologies and other) and containing almost 3000 samples collected from 230 distinct irises.
Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes
This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability.
Implications of Ocular Pathologies for Iris Recognition Reliability
This paper presents an analysis of how iris recognition is influenced by eye disease and an appropriate dataset comprising 2996 images of irises taken from 230 distinct eyes (including 184 affected by more than 20 different eye conditions).
Cataract influence on iris recognition performance
Results show a significant degradation in iris recognition reliability manifesting by worsening the genuine scores in all three matchers used in this study (12% of genuine score increase for an academic matcher, up to 175% of genuine score increase obtained for an example commercial matcher).
Human Iris Recognition in Post-mortem Subjects: Study and Database
This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death.
Post-mortem Human Iris Recognition
We found that more than 90% of irises are still correctly recognized when captured a few hours after death, and that serious iris deterioration begins approximately 22 hours later, since the recognition rate drops to a range of 13. 3-73. 3% (depending on the method used) when the cornea starts to be cloudy.
Assessment of iris recognition reliability for eyes affected by ocular pathologies
To our knowledge this is the first database of iris images for disease-affected eyes made publicly available to researchers, and the most comprehensive study of what we can expect when the iris recognition is deployed for non-healthy eyes.
Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes
This paper presents the most comprehensive analysis of iris recognition reliability in the occurrence of various biological processes happening naturally and pathologically in the human body, including aging, illnesses, and post-mortem changes to date.
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images.
Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification
Results suggest that: (a) people improve their identity verification accuracy when asked to annotate matching and non-matching regions between the pair of images, (b) images depicting the same eye with large difference in pupil dilation were the most challenging to subjects, but benefited well from the annotation-driven classification, (c) humans performed better than iris recognition algorithms when verifying genuine pairs of post-mortem and disease-affected eyes (i. e., samples showing deformations that go beyond the distortions of a healthy iris due to pupil dilation), and (d) annotation does not improve accuracy of analyzing images from identical twins, which remain confusing for people.
Presentation Attack Detection for Cadaver Iris
We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER=1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered.
Perception of Image Features in Post-Mortem Iris Recognition: Humans vs Machines
This paper explores two ways of broadening this knowledge: (a) with an eye tracker, the salient features used by humans comparing iris images on a screen are extracted, and (b) class-activation maps produced by the convolutional neural network solving the iris recognition task are analyzed.
Data-Driven Segmentation of Post-mortem Iris Images
This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of semantic segmentation.
Iris Recognition After Death
This paper presents a comprehensive study of post-mortem human iris recognition carried out for 1, 200 near-infrared and 1, 787 visible-light samples collected from 37 deceased individuals kept in the mortuary conditions.
Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art
Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized.
