Steven A. Israel

ECG to identify individuals

The electrocardiogram (ECG also called EKG) trace expresses cardiac features that are unique to an individual. The ECG processing followed a logical series of experiments with quantifiable metrics. Data filters were designed based upon the observed noise sources. Fiducial points were identified on the filtered data and extracted digitally for each heartbeat. From the fiducial points, stable features were computed that characterize the uniqueness of an individual. The tests show that the extracted features are independent of sensor location, invariant to the individuals state of anxiety, and unique to an individual.

Fusing face and ECG for personal identification

Single modality biometric identification systems exhibit performance that may not be adequate for many security applications. Face and fingerprint modalities dominate the biometric verification/identification field. However, both face and fingerprint can be compromised using counterfeit credentials. Previous research has demonstrated the use of the electrocardiogram (ECG) as a novel biometric. This paper explores the fusion of a traditional face recognition technique with ECG. System performance with multimodality fusion can be superior to reliance on a single biometric, but performance depends heavily on the fusion technique. In addition, a fusion-based system is more difficult to defeat, since an imposter must provide counterfeit credentials for both face and cardiovascular function.

A sequential procedure for individual identity verification using ECG

The electrocardiogram (ECG) is an emerging novel biometric for human identification. One challenge for the practical use of ECG as a biometric is minimizing the time needed to acquire user data. We present a methodology for identity verification that quantifies the minimum number of heartbeats required to authenticate an enrolled individual. The approach rests on the statistical theory of sequential procedures. The procedure extracts fiducial features from each heartbeat to compute the test statistics. Sampling of heartbeats continues until a decision is reached—either verifying that the acquired ECG matches the stored credentials of the individual or that the ECG clearly does not match the stored credentials for the declared identity. We present the mathematical formulation of the sequential procedure and illustrate the performance with measured data. The initial test was performed on a limited population, twenty-nine individuals. The sequential procedure arrives at the correct decision in fifteen heartbeats or fewer in all but one instance and in most cases the decision is reached with half as many heartbeats. Analysis of an additional 75 subjects measured under different conditions indicates similar performance. Issues of generalizing beyond the laboratory setting are discussed and several avenues for future investigation are identified.

Developing an interpretability scale for motion imagery

The motion imagery community would benefit from standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. Several considerations unique to motion imagery indicate that the standard methodology employed in the past for NIIRS development may not be applicable or, at a minimum, requires modifications. The dynamic nature of motion imagery introduces a number of factors that do not affect the perceived interpretability of still imagery—namely target motion and camera motion. We conducted a series of evaluations to understand and quantify the effects of critical factors. This paper presents key findings about the relationship of perceived interpretability to ground sample distance, target motion, camera motion, and frame rate. Based on these findings, we modified the scale development methodology and validated the approach. The methodology adapts the standard NIIRS development procedures to the softcopy exploitation environment and focuses on image interpretation tasks that target the dynamic nature of motion imagery. This paper describes the proposed methodology, presents the findings from a methodology assessment evaluation, and offers recommendations for the full development of a scale for motion imagery.

Factors affecting development of a motion imagery quality metric

The motion imagery community would benefit from the availability of standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. Several considerations unique to motion imagery indicate that the standard methodology employed in the past for NIIRS development may not be applicable or, at a minimum, require modifications. Traditional methods for NIIRS development rely on a close linkage between perceived image quality, as captured by specific image interpretation tasks, and the sensor parameters associated with image acquisition. The dynamic nature of motion imagery suggests that this type of linkage may not exist or may be modulated by other factors. An initial study was conducted to understand the effects target motion, camera motion, and scene complexity have on perceived image interpretability for motion imagery. This paper summarizes the findings from this evaluation. In addition, several issues emerged that require further investigation: - The effect of frame rate on the perceived interpretability of motion imagery - Interactions between color and target motion which could affect perceived interpretability - The relationships among resolution, viewing geometry, and image interpretability - The ability of an analyst to satisfy specific image exploitation tasks relative to different types of motion imagery clips Plans are being developed to address each of these issues through direct evaluations. This paper discusses each of these concerns, presents the plans for evaluations, and explores the implications for development of a motion imagery quality metric.

Methodology study for development of a motion imagery quality metric

The motion imagery community would benefit from the availability of standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. Several considerations unique to motion imagery indicate that the standard methodology employed in the past for NIIRS development may not be applicable or, at a minimum, requires modifications. The dynamic nature of motion imagery introduces a number of factors that do not affect the perceived interpretability of still imagery - namely target motion and camera motion. A set of studies sponsored by the National Geospatial-Intelligence Agency (NGA) have been conducted to understand and quantify the effects of critical factors. This study discusses the development and validation of a methodology that has been proposed for the development of a NIIRS-like scale for motion imagery. The methodology adapts the standard NIIRS development procedures to the softcopy exploitation environment and focuses on image interpretation tasks that target the dynamic nature of motion imagery. This paper describes the proposed methodology, presents the findings from a methodology assessment evaluation, and offers recommendations for the full development of a scale for motion imagery.

Heartbeat biometrics: a sensing system perspective

This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.

Quantifying interpretability for motion imagery: Applications to image chain analysis

The motion imagery community will benefit from the availability of standard measures for assessing image interpretability. The national imagery interpretability rating scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. We conducted a series of user evaluations to understand and quantify the effects of critical factors affecting the perceived interpretability of motion imagery. These evaluations provide the basis for relating perceived image interpretability to image parameters, including ground sample distance (GSD) and frame rate. The first section of this paper presents the key findings from these studies. The second portion is a new study applying these methods to quantifying information loss due to compression of motion imagery. We conducted an evaluation of several methods for video compression (JPEG2000, MPEG-2, and H.264) at various bitrates. A set of objective image quality metrics (structural similarity, peak SNR, an edge localization metric, and edge strength) were computed for the parent video clip and the various compressed products. In an evaluation, imagery analysts rated each clip relative to image interpretability tasks. The analysis quantifies the interpretability loss associated with the various compression methods and bitrates. We present the evaluation results and explore their relationship to the objective image quality metrics. The findings indicate the compression rates at which image interpretability declines significantly. These findings have implications for sensor system design, systems architecture, and mission planning.

Metrics to estimate image quality in compressed video sequences

A fundamental problem in image processing is finding objective metrics that parallel human perception of image quality. In this study, several metrics were examined to quantify compression algorithms in terms of perceived loss of image quality. In addition, we sought to describe the relationship of image quality as a function of bit rate. The compression schemes used were JPEG2000, MPEG2, and H.264. The frame size was fixed at 848x480 and the encoding varied from 6000 k bps to 200 k bps. The metrics examined were peak signal to noise ratio (PSNR), structural similarity (SSIM), edge localization metrics, and a blur metric. To varying degrees, the metrics displayed desirable properties, namely they were monotonic in the bit rate, the group of pictures (GOP) structure could be inferred, and they tended to agree with human perception of quality degradations. Additional work is being conducted to quantify the sensitivity of these measures with respect to our Motion Imagery Quality Scale.

Performance Metrics: How and When

Abstract This paper compares different classification performance metrics commonly used for environmental monitoring with remotely sensed satellite data and their associated error bounds. Discriminant functions were generated for three classifiers using common training data. Identical independent test data were classified by each discriminant function. The classified test values were evaluated by each performance metric. Each performance measure, its error bound, and its significance were computed. The performance measures were divided into three classes; those measures associated with contingency matrices, casewise comparisons, and ROC curves. The operation, results, and limitations of each metric are discussed.