Ear Biometrics: A Comprehensive Study of Taxonomy, Detection, and Recognition Methods

Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice to identify individuals in controlled or challenging environments. The outer part of the ear demonstrates high discriminative information across individuals and has shown to be robust for recognition. In addition, the data acquisition procedure is contactless, non-intrusive, and covert. This work focuses on using ear images for human authentication in visible and thermal spectrums. We perform a systematic study of the ear features and propose a taxonomy for them. Also, we investigate the parts of the head side view that provides distinctive identity cues. Following, we study the different modules of the ear recognition system. First, we propose an ear detection system that uses deep learning models. Second, we compare machine learning methods to state traditional systems\u27 baseline ear recognition performance. Third, we explore convolutional neural networks for ear recognition and the optimum learning process setting. Fourth, we systematically evaluate the performance in the presence of pose variation or various image artifacts, which commonly occur in real-life recognition applications, to identify the robustness of the proposed ear recognition models. Additionally, we design an efficient ear image quality assessment tool to guide the ear recognition system. Finally, we extend our work for ear recognition in the long-wave infrared domains

Taming the Invisible Monster: System Parameter Constraints for Epsilon Aurigae from the Far-Ultraviolet to the Mid-Infrared

We have assembled new Spitzer Space Telescope Infrared Array Camera observations of the mysterious binary star Epsilon Aurigae, along with archival far-ultraviolet to mid-infrared data, to form an unprecedented spectral energy distribution spanning three orders of magnitude in wavelength from 0.1 microns to 100 microns. The observed spectral energy distribution can be reproduced using a three component model consisting of a 2.2+0.9/-0.8 Msun F type post-asymptotic giant branch star, and a 5.9+/-0.8 Msun B5+/-1 type main sequence star that is surrounded by a geometrically thick, but partially transparent, disk of gas and dust. At the nominal HIPPARCOS parallax distance of 625 pc, the model normalization yields a radius of 135+/-5 Rsun for the F star, consistent with published interferometric observations. The dusty disk is constrained to be viewed at an inclination of i > 87 deg, and has effective temperature of 550+/-50 K with an outer radius of 3.8 AU and a thickness of 0.95 AU. The dust content of the disk must be largely confined to grains larger than ~10 microns in order to produce the observed gray optical-infrared eclipses and the lack of broad dust emission features in the archival Spitzer mid-infrared spectra. The total mass of the disk, even considering a potential gaseous contribution in addition to the dust that produces the observed infrared excess, is << 1 Msun. We discuss evolutionary scenarios for this system that could lead to the current status of the stellar components and suggests possibilities for its future evolution, as well as potential observational tests of our model.Comment: 13 pages, 3 figures. Accepted for publication in The Astrophysical Journal

Earth Observing System. Volume 1, Part 2: Science and Mission Requirements. Working Group Report Appendix

Areas of global hydrologic cycles, global biogeochemical cycles geophysical processes are addressed including biological oceanography, inland aquatic resources, land biology, tropospheric chemistry, oceanic transport, polar glaciology, sea ice and atmospheric chemistry

The abundances of polyacetylenes towards CRL618

We present a mid-infrared high spectral resolution spectrum of CRL618 in the frequency ranges 778-784 and 1227-1249 cm^-1 (8.01-8.15 and 12.75-12.85 um) taken with the Texas Echelon-cross-Echelle Spectrograph (TEXES) and the Infrared Telescope Facility (IRTF). We have identified more than 170 ro-vibrational lines arising from C2H2, HCN, C4H2, and C6H2. We have found no unmistakable trace of C8H2. The line profiles display a complex structure suggesting the presence of polyacetylenes in several components of the circumstellar envelope (CSE). We derive total column densities of 2.5 10^17, 3.1 10^17, 2.1 10^17, 9.3 10^16 cm^-2, and < 5 10^16 cm^-2 for HCN, C2H2, C4H2, C6H2, and C8H2, respectively. The observations indicate that both the rotational and vibrational temperatures in the innermost CSE depend on the molecule, varying from 100 to 350 K for the rotational temperatures and 100 to 500 K for the vibrational temperatures. Our results support a chemistry in the innermost CSE based on radical-neutral reactions triggered by the intense UV radiation field.Comment: 9 pages, 4 figures, 1 table; accepted for publication in The Astrophysical Journa

A cortical potential reflecting cardiac function

Emotional trauma and psychological stress can precipitate cardiac arrhythmia and sudden death through arrhythmogenic effects of efferent sympathetic drive. Patients with preexisting heart disease are particularly at risk. Moreover, generation of proarrhythmic activity patterns within cerebral autonomic centers may be amplified by afferent feedback from a dysfunctional myocardium. An electrocortical potential reflecting afferent cardiac information has been described, reflecting individual differences in interoceptive sensitivity (awareness of one's own heartbeats). To inform our understanding of mechanisms underlying arrhythmogenesis, we extended this approach, identifying electrocortical potentials corresponding to the cortical expression of afferent information about the integrity of myocardial function during stress. We measured changes in cardiac response simultaneously with electroencephalography in patients with established ventricular dysfunction. Experimentally induced mental stress enhanced cardiovascular indices of sympathetic activity (systolic blood pressure, heart rate, ventricular ejection fraction, and skin conductance) across all patients. However, the functional response of the myocardium varied; some patients increased, whereas others decreased, cardiac output during stress. Across patients, heartbeat-evoked potential amplitude at left temporal and lateral frontal electrode locations correlated with stress-induced changes in cardiac output, consistent with an afferent cortical representation of myocardial function during stress. Moreover, the amplitude of the heartbeat-evoked potential in the left temporal region reflected the proarrhythmic status of the heart (inhomogeneity of left ventricular repolarization). These observations delineate a cortical representation of cardiac function predictive of proarrhythmic abnormalities in cardiac repolarization. Our findings highlight the dynamic interaction of heart and brain in stress-induced cardiovascular morbidity

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Background: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance