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Department of Electrical EngineeringBiometrics such as fingerprint, iris, face, and electrocardiogram (ECG) have been investigated as convenient and powerful security tools that can potentially replace or supplement current possession or knowledge based authentication schemes. Recently, multi-spectral skin photomatrix (MSP) has been newly found as one of the biometrics. Moreover, since the interest of usage and security for wearable devices have been increasing, multi-modal biometrics authentication which is combining more than two modalities such as (iris + face) or (iris + fingerprint) for powerful and convenience authentication is widely proposed. However, one practical drawback of biometrics is irrevocability. Unlike password, biometrics can not be canceled and re-used once compromised since they are not changed forever. There have been several works on cancelable biometrics to overcome this drawback. ECG has been investigated as a promising biometrics, but there are few research on cancelable ECG biometrics. As we aim to study a way for multi-modal biometric scheme for wearable devices that is assumed circumstance under some limitations such as relatively high performance, low computing power, and limited information (not sharing users information to the public), in this study, we proposed a multi-modal biometrics authentication by combining ECG and MSP. For investigating the performances versus level of fusions, Adaboost algorithm was studied as a score level fusion method, and Majority Voting was studied as a decision level fusion method. Due to ECG signal is 1 dimensional, it provides benefits in wearable devices for overcoming the computing memory limitation. The reasons that we select MSP combination with ECG are it can be collected by measuring on inner-wrist of human body and it also can be considered as hardly stolen modality in remote ways. For proposed multi-modal biometrics, We evaluate our methods using collected data by Brain-Computer-Interface lab with 63 subjects. Our Adaboost based pro- posed multi modal biometrics method with performance boost yielded 99.7% detection probability at 0.1% false alarm ratio (PD0.1) and 0.3% equal error rate (EER), which are far better than simply combining by Majority Voting algorithm with 21.5% PD0.1 and 1.6% EER. Note that for training the Adaboost algorithm, we used only 9 people dataset which is assumed as public data and not included for testing data set, against for knowledge limitation as the other constraint. As initial step for user template protection, We proposed a cancelable ECG based user authentication using a composite hypothesis testing in compressive sensing do- main by deriving a generalized likelihood ratio test (GLRT) detector. We also pro- posed two performance boost tricks in compressive sensing domain to compensate for performance degradation due to cancelable schemes: user template guided filtering and T-wave shift model based GLRT detector for random projection domain. To verify our proposed method, we investigated cancelable biometrics criteria for the proposed methods to confirm that the proposed algorithms are indeed cancelable. For proposed cancelable ECG authentication, We evaluated our proposed methods using ECG data with 147 subjects from three public ECG data sets (ECG-ID, MIT- BIH Normal / Arrhythmia). Our proposed cancelable ECG authentication method is practically cancelable by satisfying all cancelable biometrics criteria. Moreover, our proposed method with performance boost tricks achieved 97.1% detection probability at 1% false alarm ratio (PD1) and 1.9% equal error rate (EER), which are even better than non-cancelable baseline with 94.4% PD1 and 3.1% EER for single pulse ECG authentication.ope

Cancelable ECG Biometrics using Compressive Sensing-Generalized Likelihood Ratio Test

Electrocardiogram (ECG) has been investigated as promising biometrics, but it cannot be canceled and re-used once compromised just like other biometrics. We propose methods to overcome the issue of irrevocability in ECG biometrics without compromising performance. Our proposed cancelable user authentication uses a generalized likelihood ratio test (GLRT) based on a composite hypothesis testing in compressive sensing (CS) domain We also propose a permutation-based revocation method for CS-based cancelable biometrics so that it becomes resilient to record multiplicity attack. In addition, to compensate for inevitable performance degradation due to cancelable schemes, we also propose two performance improvement methods without undermining cancelable schemes: a self-guided ECG filtering and a T-wave shift model in our CS-GLRT. Finally, our proposed methods were evaluated for various cancelable biometrics criteria with the public ECG-ID data (89 subjects). Our cancelable ECG biometric methods yielded up to 93.0% detection probability at 2.0% false alarm ratio (PD*) and 3.8% equal error rate (EER), which are comparable to or even better than non-cancelable baseline with 93.2% PD* and 4.8% EER for challenging single pulse ECG authentication, respectively. Our proposed methods met all cancelable biometrics criteria theoretically or empirically. Our cancelable secure user template with our novel revocation process is practically non-invertible and robust to record multiplicity attack

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD.1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD.1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication

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Department of Electrical Engineeringclos

Dynamic PET denoising using Robust PCA

Many denoising methods for dynamic positron emission tomography (PET) have been proposed such as conventional Gaussian spatio-temporal smoothing, Principle Component Analysis (PCA) based denoising, HighlY constrained backPRojection (HYPR), and Non-Local Means (NLM) based methods. We investigated robust PCA, that was originally proposed by Candes et al., as a potential alternative for dynamic PET denoising. Robust PCA decomposes a large data matrix M into the sum of a low rank matrix L and a sparse matrix S by an iterative optimization procedure. We conjecture that slowly varying activity over time corresponds to low rank components L and noise / sudden activity changes can be represented as sparse components S. We performed robust PCA for 4-D dynamic PET (3-D volume with time) and slice-by-slice dynamic PET (2-D multi-slice with time) with 18 F dynamic PET data (126 x 126 x 117 voxels with 54 frames) from an animal study with two rats. Our proposed methods yield promising decomposition results for dynamic PET denoising. Our robust PCA with 4-D PET data yielded more temporally smooth low rank volume sequences than slice-by-slice dynamic PET. Initial visual quality assessment confirmed our conjecture and indicated that robust PCA does not only separate noise in sparse matrix S, but also separate sudden activity changes such as initial injection of activity unlike other methods such as 4-D Gaussian or HYPR. Further quantitative analysis with Monte Carlo simulation will be required to fairly compare our proposed methods with previous dynamic PET denoising methods

Cancelable ECG Biometrics using GLRT and Performance Improvement using Guided Filter with Irreversible Guide Signal

Biometrics such as ECG provides a convenient and powerful security tool to verify or identify an individual. However, one important drawback of biometrics is that it is irrevocable. In other words, biometrics cannot be re-used practically once it is compromised. Cancelable biometrics has been investigated to overcome this drawback. In this paper, we propose a cancelable ECG biometrics by deriving a generalized likelihood ratio test (GLRT) detector from a composite hypothesis testing in randomly projected domain. Since it is common to observe performance degradation for cancelable biometrics, we also propose a guided filtering (GF) with irreversible guide signal that is a non-invertibly transformed signal of ECG authentication template. We evaluated our proposed method using ECG-ID database with 89 subjects. Conventional Euclidean detector with original ECG template yielded 93.9% PD1 (detection probability at 1% FAR) while Euclidean detector with 10% compressed ECG (1/10 of the original data size) yielded 90.8% PD1. Our proposed GLRT detector with 10% compressed ECG yielded 91.4%, which is better than Euclidean with the same compressed ECG. GF with our proposed irreversible ECG template further improved the performance of our GLRT with 10% compressed ECG up to 94.3%, which is higher than Euclidean detector with original ECG. Lastly, we showed that our proposed cancelable ECG biometrics practically met cancelable biometrics criteria such as efficiency, re-usability, diversity and non-invertibility

Preliminary Studies on Training and Fine-Tuning Deep Denoiser Neural Networks in Learned D-Amp for Undersampled Real Mr Measurements

Recently, deep learning based MR image reconstructions have shown outstanding performance. While there have been many direct mapping based methods by deep neural networks without taking advantage of known physical model of medical imaging modality, some groups investigated combining conventional model-based image reconstruction (MBIR) and learning based method to enhance performance and computation speed of MBIR. Here, we investigated learned denoiser-based approximate message passing (LDAMP) with undersampled MR measurements. LDAMP yielded favorable performance over BM3D-based AMP even though ground truth images were noisy and deep denoisers were trained only for Gaussian noise, not for undersampling artifacts. We further investigated the feasibility of using Stein's unbiased risk estimator (SURE) to fine-tune deep denoisers with given undersampled MR measurement. Our proposed SURE based unsupervised fine-tuning method faithfully reconstructed images corresponding to the measurement and demonstrated the potential of enhancing the image quality of LDAMP results on real MRI dataset

Physiology-base d augmente d deep neural network frameworks for ECG biometrics with short ECG pulses considering varying heart rates

Electrocardiogram (ECG) has been investigated as promising biometrics with high authentication accuracy, natural liveness test ability, and wearable sensor availability. There have been many algorithms developed for ECG biometric authentication or identification including recent state-of-the-art deep learning (DL) methods that usually yielded excellent performance with real ECG data in ideal conditions. However, one of the challenges against ideal conditions is the intra-personal variability of ECG pulses due to heart beat rate changes. Due to this variability, ECG based biometric methods have experienced significant performance degradation. It is especially challenging when a small number of ECG pulses must be used for biometrics with fast response authentication since there is not enough information available to correct for different heart rates. In this letter, we investigated DL based ECG biometrics with the input of a small number of ECG pulses considering varying heart rates. We propose physiology-based augmented deep neural network (DNN) frameworks for ECG biometric methods that are based on the Hodges' QT interval correction. Unlike QT interval correction methods, our proposed framework does not require the estimated heart rate. Our proposed training and testing schemes were evaluated with representative DL based biometric methods using CNN and RNN with very short ECG pulses (1 or 3 pulses per authentication) from the public multi-session ECG-ID dataset (83 subjects). We exploited the ECG-ID dataset to simulate the challenging scenario including the enrollment and authentication happening over relatively long time duration so that heart rate variation is likely occurring. Our augmented DNN frameworks yielded significantly better performance than the original DL based biometrics; up to 11.7% improvement in accuracy and 8.6% improvement in sensitivity simultaneously with 99.9% specificity.(c) 2022 Elsevier B.V. All rights reserved

PURECOMB: POISSON UNBIASED RISK ESTIMATOR BASED ENSEMBLE OF SELF-SUPERVISED DEEP DENOISERS FOR CLINICAL BONE SCAN IMAGE

Bone scan is a clinical practice which is performed in nuclear medicine to evaluate skeletal lesions or bone metastases. Reducing scan time is desirable due to faster throughput of gamma camera and reducing potential patient movement, but leads to increased noise. Some of the recent self-supervised deep denoisers such as Noise2Noise (N2N) and Poisson unbiased risk estimator (PURE) can be good candidates for reducing Poisson noise in nuclear medicine planar images. Here we investigate self-supervised deep denoisers for Poisson noise to boost the performance of denoising. Firstly, we propose to extend PURE to accommodate two correlated noisy images (ePURE) to self-supervisedly train a deep denoiser. Then, we propose PUREmap that measures the uncertainty of incoming noisy input image to ensemble the outputs of deep denoisers trained with N2N and our ePURE. Our proposed method was evaluated with whole body planar bone scans of 326 patients (200 for training and 126 for testing) with and without lesions, yielding comparable denoising performance only with 20% of full count to the deep denoiser that was supervisedly trained with full count images (N2F) while showing lower uncertainty on various count level (5% similar to 30%) compared to N2F.N