October 16, 2019

3493 words 17 mins read

Paper Group ANR 1051

Propensity score estimation using classification and regression trees in the presence of missing covariate data. An Analysis of Classical Multidimensional Scaling. Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes. Sparse Nonnegative CANDECOMP/PARAFAC Decomposition in Block Coordinate De …

Propensity score estimation using classification and regression trees in the presence of missing covariate data


Title	Propensity score estimation using classification and regression trees in the presence of missing covariate data
Authors	Bas B. L. Penning de Vries, Maarten van Smeden, Rolf H. H. Groenwold
Abstract	Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because some implementations allow for incomplete records to be incorporated in the tree fitting and provide propensity score estimates for all subjects. Based on theoretical considerations, we argue that the automatic handling of missing data by CART may however not be appropriate. Using a series of simulation experiments, we examined the performance of different approaches to handling missing covariate data; (i) applying the CART algorithm directly to the (partially) incomplete data, (ii) complete case analysis, and (iii) multiple imputation. Performance was assessed in terms of bias in estimating exposure-outcome effects \add{among the exposed}, standard error, mean squared error and coverage. Applying the CART algorithm directly to incomplete data resulted in bias, even in scenarios where data were missing completely at random. Overall, multiple imputation followed by CART resulted in the best performance. Our study showed that automatic handling of missing data in CART can cause serious bias and does not outperform multiple imputation as a means to account for missing data.
Tasks	Imputation
Published	2018-07-25
URL	http://arxiv.org/abs/1807.09462v1
PDF	http://arxiv.org/pdf/1807.09462v1.pdf
PWC	https://paperswithcode.com/paper/propensity-score-estimation-using
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An Analysis of Classical Multidimensional Scaling


Title	An Analysis of Classical Multidimensional Scaling
Authors	Anna Little, Yuying Xie, Qiang Sun
Abstract	Classical multidimensional scaling is an important tool for dimension reduction in many applications. Yet few theoretical results characterizing its statistical performance exist. In this paper, we provide a theoretical framework for analyzing the quality of embedded samples produced by classical multidimensional scaling. This lays down the foundation for various downstream statistical analysis. As an application, we study its performance in the setting of clustering noisy data. Our results provide scaling conditions on the sample size, ambient dimensionality, between-class distance and noise level under which classical multidimensional scaling followed by a clustering algorithm can recover the cluster labels of all samples with high probability. Numerical simulations confirm these scaling conditions are sharp in low, moderate, and high dimensional regimes. Applications to both human RNAseq data and natural language data lend strong support to the methodology and theory.
Tasks	Dimensionality Reduction
Published	2018-12-31
URL	http://arxiv.org/abs/1812.11954v3
PDF	http://arxiv.org/pdf/1812.11954v3.pdf
PWC	https://paperswithcode.com/paper/an-analysis-of-classical-multidimensional
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Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes


Title	Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes
Authors	Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Abstract	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. Insightful conclusions are offered in relation to all three of these aspects. Extensive regression analysis of the template aging phenomenon shows that differences in pupil dilation, combined with certain quality factors of the sample image and the progression of time itself can significantly degrade recognition accuracy. Impactful effects can also be observed when iris recognition is employed with eyes affected by certain eye pathologies or (even more) with eyes of the deceased subjects. Notably, appropriate databases are delivered to the biometric community to stimulate further research in these utterly important areas of iris biometrics studies. Finally, some open questions are stated to inspire further discussions and research on these important topics. To Authors’ best knowledge, this is the only scientific study of iris recognition reliability of such a broad scope and novelty.
Tasks	Iris Recognition
Published	2018-09-01
URL	http://arxiv.org/abs/1809.00182v1
PDF	http://arxiv.org/pdf/1809.00182v1.pdf
PWC	https://paperswithcode.com/paper/iris-recognition-under-biologically
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Sparse Nonnegative CANDECOMP/PARAFAC Decomposition in Block Coordinate Descent Framework: A Comparison Study


Title	Sparse Nonnegative CANDECOMP/PARAFAC Decomposition in Block Coordinate Descent Framework: A Comparison Study
Authors	Deqing Wang, Fengyu Cong, Tapani Ristaniemi
Abstract	Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful nonnegative and sparse components. Thus, an optimization method for NCP that can impose sparsity efficiently is required. In this paper, we construct NCP with sparse regularization (sparse NCP) by l1-norm. Several popular optimization methods in block coordinate descent framework are employed to solve the sparse NCP, all of which are deeply analyzed with mathematical solutions. We compare these methods by experiments on synthetic and real tensor data, both of which contain third-order and fourth-order cases. After comparison, the methods that have fast computation and high effectiveness to impose sparsity will be concluded. In addition, we proposed an accelerated method to compute the objective function and relative error of sparse NCP, which has significantly improved the computation of tensor decomposition especially for higher-order tensor.
Tasks
Published	2018-12-27
URL	http://arxiv.org/abs/1812.10637v1
PDF	http://arxiv.org/pdf/1812.10637v1.pdf
PWC	https://paperswithcode.com/paper/sparse-nonnegative-candecompparafac
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Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation


Title	Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation
Authors	Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt
Abstract	Parent selection in evolutionary algorithms for multi-objective optimisation is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points. We propose to refine the parent selection on evolutionary multi-objective optimisation with diversity-based metrics. The aim is to focus on individuals with a high diversity contribution located in poorly explored areas of the search space, so the chances of creating new non-dominated individuals are better than in highly populated areas. We show by means of rigorous runtime analysis that the use of diversity-based parent selection mechanisms in the Simple Evolutionary Multi-objective Optimiser (SEMO) and Global SEMO for the well known bi-objective functions ${\rm O{\small NE}M{\small IN}M{\small AX}}$ and ${\rm LOTZ}$ can significantly improve their performance. Our theoretical results are accompanied by experimental studies that show a correspondence between theory and empirical results and motivate further theoretical investigations in terms of stagnation. We show that stagnation might occur when favouring individuals with a high diversity contribution in the parent selection step and provide a discussion on which scheme to use for more complex problems based on our theoretical and experimental results.
Tasks
Published	2018-05-03
URL	http://arxiv.org/abs/1805.01221v2
PDF	http://arxiv.org/pdf/1805.01221v2.pdf
PWC	https://paperswithcode.com/paper/design-and-analysis-of-diversity-based-parent
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Linear regression analysis of template aging in iris biometrics


Title	Linear regression analysis of template aging in iris biometrics
Authors	Mateusz Trokielewicz
Abstract	The aim of this work is to determine how vulnerable different iris coding methods are in relation to biometric template aging phenomenon. This is considered to be particularly important when the time lapse between gallery and probe samples extends significantly, to more than a few years. Our experiments employ iris aging analysis conducted using three different iris recognition algorithms and a database of 583 samples from 58 irises collected up to nine years apart. To determine the degradation rates of similarity scores with extending time lapse and also in relation to multiple image quality and geometrical factors of sample images, a linear regression analysis was performed. 29 regression models have been tested with both the time parameter and geometrical factors being statistically significant in every model. Quality measures that showed statistically significant influence on the predicted variable were, depending on the method, image sharpness and local contrast or their mutual relations. To our best knowledge, this is the first paper describing aging analysis using multiple regression models with data covering such a wide time period. Results presented suggest that template aging effect occurs in iris biometrics to a statistically significant extent. Image quality and geometrical factors may contribute to the degradation of similarity score. However, the estimate of time parameter showed statistical significance and similar value in each of the tested models. This reveals that the aging phenomenon may as well be unrelated to quality and geometrical measures of the image.
Tasks	Iris Recognition
Published	2018-09-01
URL	http://arxiv.org/abs/1809.00170v1
PDF	http://arxiv.org/pdf/1809.00170v1.pdf
PWC	https://paperswithcode.com/paper/linear-regression-analysis-of-template-aging
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Implications of Ocular Pathologies for Iris Recognition Reliability


Title	Implications of Ocular Pathologies for Iris Recognition Reliability
Authors	Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Abstract	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). The images were collected in near infrared and visible light during routine ophthalmological examination. The experimental study carried out utilizing four independent iris recognition algorithms (MIRLIN, VeriEye, OSIRIS and IriCore) renders four valuable results. First, the enrollment process is highly sensitive to those eye conditions that obstruct the iris or cause geometrical distortions. Second, even those conditions that do not produce visible changes to the structure of the iris may increase the dissimilarity between samples of the same eyes. Third, eye conditions affecting the geometry or the tissue structure of the iris or otherwise producing obstructions significantly decrease same-eye similarity and have a lower, yet still statistically significant, influence on impostor comparison scores. Fourth, for unhealthy eyes, the most prominent effect of disease on iris recognition is to cause segmentation errors. To our knowledge this paper describes the largest database of iris images for disease-affected eyes made publicly available to researchers and offers the most comprehensive study of what we can expect when iris recognition is employed for diseased eyes.
Tasks	Iris Recognition
Published	2018-09-01
URL	http://arxiv.org/abs/1809.00168v1
PDF	http://arxiv.org/pdf/1809.00168v1.pdf
PWC	https://paperswithcode.com/paper/implications-of-ocular-pathologies-for-iris
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Unlimited Road-scene Synthetic Annotation (URSA) Dataset


Title	Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Authors	Matt Angus, Mohamed ElBalkini, Samin Khan, Ali Harakeh, Oles Andrienko, Cody Reading, Steven Waslander, Krzysztof Czarnecki
Abstract	In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datasets. The limited availability of such data is due to the time cost and human effort required to accurately and consistently label real images on a pixel level. Modern sandbox video game engines provide open world environments where traffic and pedestrians behave in a pseudo-realistic manner. This caters well to the collection of a believable road-scene dataset. Utilizing open-source tools and resources found in single-player modding communities, we provide a method for persistent, ground truth, asset annotation of a game world. By collecting a synthetic dataset containing upwards of $1,000,000$ images, we demonstrate real-time, on-demand, ground truth data annotation capability of our method. Supplementing this synthetic data to Cityscapes dataset, we show that our data generation method provides qualitative as well as quantitative improvements—for training networks—over previous methods that use video games as surrogate.
Tasks	Semantic Segmentation
Published	2018-07-16
URL	http://arxiv.org/abs/1807.06056v1
PDF	http://arxiv.org/pdf/1807.06056v1.pdf
PWC	https://paperswithcode.com/paper/unlimited-road-scene-synthetic-annotation
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The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments


Title	The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments
Authors	Luiz A. Zanlorensi, Eduardo Luz, Rayson Laroca, Alceu S. Britto Jr., Luiz S. Oliveira, David Menotti
Abstract	The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible spectrum under unconstrained environments. In this scenario, the acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems. Besides delineating the iris region, usually preprocessing techniques such as normalization and segmentation of noisy iris images are employed to minimize these problems. But these techniques inevitably run into some errors. In this context, we propose the use of deep representations, more specifically, architectures based on VGG and ResNet-50 networks, for dealing with the images using (and not) iris segmentation and normalization. We use transfer learning from the face domain and also propose a specific data augmentation technique for iris images. Our results show that the approach using non-normalized and only circle-delimited iris images reaches a new state of the art in the official protocol of the NICE.II competition, a subset of the UBIRIS database, one of the most challenging databases on unconstrained environments, reporting an average Equal Error Rate (EER) of 13.98% which represents an absolute reduction of about 5%.
Tasks	Data Augmentation, Iris Recognition, Iris Segmentation, Transfer Learning
Published	2018-08-29
URL	http://arxiv.org/abs/1808.10032v1
PDF	http://arxiv.org/pdf/1808.10032v1.pdf
PWC	https://paperswithcode.com/paper/the-impact-of-preprocessing-on-deep
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Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection


Title	Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Authors	Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng, Jianwen Xie, Yi Niu, Shiliang Pu, Fei Wu
Abstract	This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only supervision of video-level labels and makes prediction of intervals of multiple actions. Specifically, we first assemble video clips according to class labels by an attention mechanism that learns class-variable attention weights and thus helps the noise relieving from background or other actions. Secondly, we build temporal relationship between actions by feeding the assembled features into an enhanced recurrent neural network. Finally, we transform the output of recurrent neural network into the corresponding action distribution. In order to generate more precise temporal proposals, we design a score term called segregated temporal gradient-weighted class activation mapping (ST-GradCAM) fused with attention weights. Experiments on THUMOS’14 and ActivityNet1.3 datasets show that our approach outperforms the state-of-the-art weakly-supervised method, and performs at par with the fully-supervised counterparts.
Tasks	Action Detection, Multiple Action Detection
Published	2018-11-19
URL	http://arxiv.org/abs/1811.07460v1
PDF	http://arxiv.org/pdf/1811.07460v1.pdf
PWC	https://paperswithcode.com/paper/segregated-temporal-assembly-recurrent
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Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification


Title	Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification
Authors	Daniel Moreira, Mateusz Trokielewicz, Adam Czajka, Kevin W. Bowyer, Patrick J. Flynn
Abstract	This paper advances the state of the art in human examination of iris images by (1) assessing the impact of different iris conditions in identity verification, and (2) introducing an annotation step that improves the accuracy of people’s decisions. In a first experimental session, 114 subjects were asked to decide if pairs of iris images depict the same eye (genuine pairs) or two distinct eyes (impostor pairs). The image pairs sampled six conditions: (1) easy for algorithms to classify, (2) difficult for algorithms to classify, (3) large difference in pupil dilation, (4) disease-affected eyes, (5) identical twins, and (6) post-mortem samples. In a second session, 85 of the 114 subjects were asked to annotate matching and non-matching regions that supported their decisions. Subjects were allowed to change their initial classification as a result of the annotation process. 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.
Tasks	Iris Recognition
Published	2018-07-13
URL	http://arxiv.org/abs/1807.05245v2
PDF	http://arxiv.org/pdf/1807.05245v2.pdf
PWC	https://paperswithcode.com/paper/performance-of-humans-in-iris-recognition-the
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From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities


Title	From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities
Authors	Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali
Abstract	Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in “Radiomics”. Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Nevertheless, recent advancements in deep learning have caused trends towards deep learning-based Radiomics (also referred to as discovery Radiomics). Considering the advantages of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple data sources. Considering the variety of approaches to Radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This manuscript provides a unique interdisciplinary perspective on Radiomics by discussing state-of-the-art signal processing solutions in the context of Radiomics.
Tasks
Published	2018-08-23
URL	http://arxiv.org/abs/1808.07954v3
PDF	http://arxiv.org/pdf/1808.07954v3.pdf
PWC	https://paperswithcode.com/paper/from-hand-crafted-to-deep-learning-based
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Data-Driven Segmentation of Post-mortem Iris Images


Title	Data-Driven Segmentation of Post-mortem Iris Images
Authors	Mateusz Trokielewicz, Adam Czajka
Abstract	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. Post-mortem iris recognition has recently emerged as an alternative, or additional, method useful in forensic analysis. At the same time it poses many new challenges from the technological standpoint, one of them being the image segmentation stage, which has proven difficult to be reliably executed by conventional iris recognition methods. Our approach is based on the SegNet architecture, fine-tuned with 1,300 manually segmented post-mortem iris images taken from the Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in this paper show that this data-driven solution is able to learn specific deformations present in post-mortem samples, which are missing from alive irises, and offers a considerable improvement over the state-of-the-art, conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU) metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in this paper) averaged over subject-disjoint, multiple splits of the data into train and test subsets. This paper offers the first known to us method of automatic processing of post-mortem iris images. We offer source codes with the trained DCNN that perform end-to-end segmentation of post-mortem iris images, as described in this paper. Also, we offer binary masks corresponding to manual segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to facilitate development of alternative methods for post-mortem iris segmentation.
Tasks	Iris Recognition, Iris Segmentation, Semantic Segmentation
Published	2018-07-11
URL	http://arxiv.org/abs/1807.04154v1
PDF	http://arxiv.org/pdf/1807.04154v1.pdf
PWC	https://paperswithcode.com/paper/data-driven-segmentation-of-post-mortem-iris
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Smoothed Action Value Functions for Learning Gaussian Policies


Title	Smoothed Action Value Functions for Learning Gaussian Policies
Authors	Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans
Abstract	State-action value functions (i.e., Q-values) are ubiquitous in reinforcement learning (RL), giving rise to popular algorithms such as SARSA and Q-learning. We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q-value. We show that such smoothed Q-values still satisfy a Bellman equation, making them learnable from experience sampled from an environment. Moreover, the gradients of expected reward with respect to the mean and covariance of a parameterized Gaussian policy can be recovered from the gradient and Hessian of the smoothed Q-value function. Based on these relationships, we develop new algorithms for training a Gaussian policy directly from a learned smoothed Q-value approximator. The approach is additionally amenable to proximal optimization by augmenting the objective with a penalty on KL-divergence from a previous policy. We find that the ability to learn both a mean and covariance during training leads to significantly improved results on standard continuous control benchmarks.
Tasks	Continuous Control, Q-Learning
Published	2018-03-06
URL	http://arxiv.org/abs/1803.02348v3
PDF	http://arxiv.org/pdf/1803.02348v3.pdf
PWC	https://paperswithcode.com/paper/smoothed-action-value-functions-for-learning
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Cross-spectral Iris Recognition for Mobile Applications using High-quality Color Images


Title	Cross-spectral Iris Recognition for Mobile Applications using High-quality Color Images
Authors	Mateusz Trokielewicz, Ewelina Bartuzi
Abstract	With the recent shift towards mobile computing, new challenges for biometric authentication appear on the horizon. This paper provides a comprehensive study of cross-spectral iris recognition in a scenario, in which high quality color images obtained with a mobile phone are used against enrollment images collected in typical, near-infrared setups. Grayscale conversion of the color images that employs selective RGB channel choice depending on the iris coloration is shown to improve the recognition accuracy for some combinations of eye colors and matching software, when compared to using the red channel only, with equal error rates driven down to as low as 2%. The authors are not aware of any other paper focusing on cross-spectral iris recognition is a scenario with near-infrared enrollment using a professional iris recognition setup and then a mobile-based verification employing color images.
Tasks	Iris Recognition
Published	2018-07-11
URL	http://arxiv.org/abs/1807.04061v1
PDF	http://arxiv.org/pdf/1807.04061v1.pdf
PWC	https://paperswithcode.com/paper/cross-spectral-iris-recognition-for-mobile
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