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IEEE Workshop on Computational Intelligence in biometrics: Theory, Algorithms, and Applications, CIB

作者： Zhi Zhou Yingzi Du Craig Belcher Biometrics and pattern recognition laboratory in the Electrical and Computer Engineering Indiana University-Purdue University Indianapolis IN USA

Non-ideal iris images can significantly affect the accuracy of iris recognition systems for two reasons: 1) they cannot be properly preprocessed by the system; and/or 2) they have poor image quality. However, many traditional iris recognition systems have been deployed in law enforcement, military, or many other important locations. It will be expensive to replace all these systems. It will be desirable if the traditional systems can be transformed to perform in non-ideal situations without an expensive update. In this paper, we propose a method that can help traditional iris recognition systems to work on the non-ideal situation using a video image approach. The proposed method will quickly identify and eliminate the bad quality images from iris videos for further processing. The segmentation accuracy is critical in recognition and would be challenging for traditional systems. The segmentation evaluation is designed to evaluate if the segmentation is valid. The information distance based quality measure is used to evaluate if the image has enough quality for recognition. The segmentation evaluation score and quality score are combined to predict the recognition performance. The research results show that the proposed methods can work effectively and objectively. The combination of segmentation and quality scores is highly correlated with the recognition accuracy and can be used to improve the performance of iris recognition systems in a non-ideal situation. The deployment of such a system would not cost much since the core parts of the traditional systems are not changed and we only need to add software modules. It will be very practical to transform the traditional system using the proposed method.

关键词： Iris recognition Quantum cellular automata Quantum dots Fault tolerance Coupling circuits Quantum mechanics Electrons Manufacturing Logic testing Costs

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Multi-level iris video image thresholding

Multi-level iris video image thresholding

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IEEE Workshop on Computational Intelligence in biometrics: Theory, Algorithms, and Applications, CIB

作者： Yingzi Du N. Luke Thomas Emrah Arslanturk Biometrics and pattern recognition laboratory in the Electrical and Computer Engineering Department Indiana University-Purdue University Indianapolis IN USA

Iris recognition has been shown to be one of the most accurate biometrics. However, under non-ideal situations, its recognition accuracy can be reduced dramatically. Under such situations, video images can be used to improve the accuracy. The traditional single image based segmentation method could be inefficient. Video image based thresholding method can help improve the segmentation efficiency. However, traditional thresholding methods are not designed for iris images. In this paper, the multi-level iris video image thresholding method is proposed. It takes advantage of the correlations between consecutive images for video based thresholding. It is an orientation invariant thresholding scheme. K-mean clustering is used to find the clusters and PCA is used to quickly project the image to the clusters. The experimental results show the proposed method is effective. The thresholded images show clear pupil and iris areas, which can help further iris segmentation and processing. In addition, the proposed method can be applied to non-video images if they are obtained from the same sensor with similar illumination conditions.

关键词： Image segmentation Iris recognition Histograms biometrics Lighting Entropy Focusing Design methodology Principal component analysis Image sensors

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Incremental learning in nonstationary environments with controlled forgetting

Incremental learning in nonstationary environments with cont...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ryan Elwell Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Department Rowan University Glassboro NJ USA

We have recently introduced an incremental learning algorithm, called Learn ++ .NSE, designed for Non-Stationary Environments (concept drift), where the underlying data distribution changes over time. With each dataset drawn from a new environment, Learn ++ .NSE generates a new classifier to form an ensemble of classifiers. The ensemble members are combined through a dynamically weighted majority voting, where voting weights are determined based on classifiers' age-adjusted accuracy on current and past environments. Unlike other ensemble-based concept drift algorithms, Learn ++ .NSE does not discard prior classifiers, allowing potentially cyclical environments to be learned more effectively. While Learn ++ .NSE has been shown to work well on a variety of concept drift problems, a potential shortcoming of this approach is the cumulative nature of the ensemble size. In this contribution, we expand our analysis of the algorithm to include various ensemble pruning methods to introduce controlled forgetting. Error or age-based pruning methods have been integrated into the algorithm to prevent potential out-voting from irrelevant classifiers or simply to save memory over an extended period of time. Here, we analyze the tradeoff between these precautions and the desire to handle recurring contexts (cyclical data). Comparisons are made using several scenarios that introduce various types of drift.

关键词： Neutron spin echo Algorithm design and analysis Voting

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Early diagnosis of dementia based on intersubject whole-brain dissimilarities

Early diagnosis of dementia based on intersubject whole-brai...

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IEEE International Symposium on Biomedical Imaging

作者： S. Klein M. Loog F. van der Lijn T. den Heijer A. Hammers M. de Bruijne A. van der Lugt R.P.W. Duin M.M.B. Breteler W.J. Niessen Biomedical Imaging Group Rotterdam Departments of Radiology & Medical Informatics Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Pattern Recognition Laboratory Faculty of Electrical Engineering Mathematics Computer Science Delft University of Technnology Netherlands Department of Neurology Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Neurodis Foundation CERMEP PET Centre Lyon France MRC Clinical Sciences Centre and Division of Neuroscience and Mental Health Imperial College Hammersmith Hospital London UK Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Radiology Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Department of Epidemiology & Biostatistics Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Imaging Science & Technology Department of Applied Sciences Delft University of Technnology Netherlands

ISBN: (纸本)9781424441259

This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81% was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia.

关键词： Dementia Brain pattern recognition Shape Radiology computer science Biomedical imaging Senior citizens Diseases Biomarkers

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ERP based decision fusion for AD diagnosis across cohorts

ERP based decision fusion for AD diagnosis across cohorts

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Metin Ahiskali Deborah Green John Kounios Christopher M. Clark Robi Polikar Signal Processing and Pattern Recognition Laboratory Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA Department of Psychology Drexel University Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

As the average life expectancy increases, particularly in developing countries, prevalence of neurodegenerative diseases has also increased. This trend is especially alarming for Alzheimer's disease (AD); as there is no cure to stop or reverse the effects of AD. However, recent pharmacological advances can slow the progression of AD, but only if AD is diagnosed at early stages. We have previously introduced an ensemble of classifiers based approach for combining event related potentials obtained from different electrode locations as an effective approach for early diagnosis of AD. We further expand this approach and analyze its robustness and stability in two ways: comparing the diagnostic accuracy on hand selected and cleaned data vs. standard automated preprocessing, but more importantly, comparing the diagnostic accuracy on two different cohorts, whose data are collected under different settings: a research university lab and a community clinic.

关键词： Enterprise resource planning Proteins Biomedical signal processing Hospitals Alzheimer's disease Electrodes Robust stability Medical diagnostic imaging Biomarkers Electroencephalography

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EEG and MRI data fusion for early diagnosis of Alzheimer's disease

EEG and MRI data fusion for early diagnosis of Alzheimer's d...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tejash Patel Robi Polikar Christos Davatzikos Christopher M. Clark Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Engineering Department Rowan University Glassboro NJ USA Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promising to reduce the rate of progression. On the other hand, the efficacy of these new medications critically depends on our ability to diagnose AD at the earliest stage. Currently AD is diagnosed through longitudinal clinical evaluations, which are available only at specialized dementia clinics, hence beyond financial and geographic reach of most patients. Automated diagnosis tools that can be made available to community hospitals would therefore be very beneficial. To that end, we have previously shown that the event related potentials obtained from different scalp locations can be effectively used for early diagnosis of AD using an ensemble of classifiers based decision fusion approach. In this study, we expand our data fusion approach to include MRI based measures of regional brain atrophy. Our initial results indicate that ERPs and MRI carry complementary information, and the combination of these heterogeneous data sources using a decision fusion approach can significantly improve diagnostic accuracy.

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Random Feature Subset Selection for Analysis of Data with Missing Features

Random Feature Subset Selection for Analysis of Data with Mi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Joseph DePasquale Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Rowan University Glassboro NJ USA

We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in part by the incremental learning algorithm, Learn ++ . The premise is to generate an adequately large number of classifiers, each trained on a different and random combination of features, drawn from an iteratively updated distribution. To classify an instance with missing features, only those classifiers whose training data did not include the currently missing feature are used. These classifiers are combined by using a majority voting combination rule to obtain the final classification of the given instance. We had previously presented preliminary results on a similar approach, which could handle up to 10% missing data. In this study, we expand our work to include different types of rules to update the distribution, and also examine the effect of the algorithm's primary free parameter (the number of features used to train the ensemble of classifiers) on the overall classification performance. We show that this algorithm can now accommodate up to 30% of features missing without a significant drop in performance.

关键词： Data analysis Neural networks Training data Clustering algorithms Fuzzy neural networks USA Councils Algorithm design and analysis Iterative algorithms Random number generation Voting

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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Face recognition System Using Ant Colony Optimization-Based Selected Features

Face Recognition System Using Ant Colony Optimization-Based ...

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IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA

作者： Hamidreza Rashidy Kanan Karim Faez Mehdi Hosseinzadeh Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Tehran Polytechnic Amirkabir University of Technology슠 Tehran Iran Department of Computer Engineering University of Isfahan Isfahan Iran

Feature selection (FS) is a most important step which can affect the performance of pattern recognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm

关键词： Face recognition Ant colony optimization Discrete wavelet transforms pattern recognition Artificial intelligence Particle swarm optimization Computational intelligence computer security Application software Image processing

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Ensemble Based Data Fusion from Parietal Region Event Related Potentials for Early Diagnosis of Alzheimer's Disease

Ensemble Based Data Fusion from Parietal Region Event Relate...

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International Joint Conference on Neural Networks (IJCNN)

作者： Brian A. Balut Matthew T. Karnick Deborah Green John Kounios Christopher M. Clark Robi Polikar Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Engineering Department Rowan University Glassboro NJ USA Department of Psychology Drexel University Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

As a natural consequence of steady increase of average population age in developed countries, Alzheimer's disease is becoming an increasingly important public health concern. The financial and emotional toll of the disease is exacerbated with lack of standard diagnostic procedures available at the community clinics and hospitals, where most patients are evaluated. In our recent preliminary results, we have reported that the event related potentials (ERPs) of the electroencephalogram can be used to train an ensemble-based classifier for automated diagnosis of Alzheimer's disease. In this study, we present an updated alternative approach by combining complementary information provided by ERPs obtained from several parietal region electrodes. The results indicate that ERPs obtained from parietal region of the cortex carry substantial complementary diagnostic information. Specifically, the diagnostic ability of such an approach is substantially better, compared to the performance obtained by using data from any of the individual electrodes alone. Furthermore, the diagnostic performance of the proposed approach compares very favorably to that obtained at community clinics and hospitals.

关键词： Alzheimer's disease Enterprise resource planning Hospitals Electrodes Senior citizens Biomarkers Protocols Delay Neural networks USA Councils

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