Senior Program Director, Human Sleep Research Program, Center for Health Sciences
Search results for: fiona baker
SRI’s Dr. Fiona Baker is featured discussing the differences between childhood insomnia and sleep deprivation.
Social connectedness, sleep, physical activity associated with better mental health among youth during the COVID-19 pandemic
Dr. Fiona Baker and Dr. Orsolya Kiss add insight into the findings from the COVID data collection of the ABCD study.
SRI’s Fiona Baker, Ph.D., discusses the ABCD study and how to protect youth during stressful times.
The risk of depression in emerging adults suddenly tripled during COVID-19, and young women are particularly vulnerable
SRI’s Dr. Fiona Baker speaks on the results of a recent study on the increased risk of depression during the COVID 19 pandemic.
Physiological Synchrony: A New Approach Toward Identifying Unknown Presentation Attacks on Biometric Systems
Presentation attacks are falsified biometric traits presented on biometric systems to deceive them. While biometric systems can be tuned and modified to reliably detect known presentation attacks, their performance significantly degrades when encountering unknown presentation attacks. Here, we propose a new approach toward detecting unknown presentation attacks based on the measurement and characterization of synchrony between multiple physiological signals obtained from contact and contactless sensors. Synchrony between two physiological signals was captured by analyzing the blood flow dynamics and respiration patterns. The instantaneous phase difference between two physiological signals was represented as a phase vector using the Hilbert transform and the degree of phase coherence defined as the absolute mean of phase vectors over the analysis period was used as a measure of synchrony. A weighted k-nearest neighbors classifier was then designed to detect valid and invalid biometric presentations based on the degree of phase coherence. The proposed method was validated on the detection of synchrony between two respiration patterns obtained through the measurement of chest movements using an ultra-wideband radar and respiratory sinus arrhythmia using a finger photoplethysmogram sensor on data collected from 50 individuals. It achieved a high accuracy of 95.3%, sensitivity of 96%, and specificity of 94% in detecting corrupted and nonsynchronous patterns that did not contain valid respiration signatures. The proposed method shows promise toward improving the reliability of biometric systems in the detection of unknown and sophisticated attacks that may spoof one or more of the presented biometrics.