Biometrics beyond the visible spectrum: Imaging technologies and applications

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04391-8_20Proceedings of Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid (Spain)Human body images acquired at visible spectrum have inherent restrictions that hinder the performance of person recognition systems built using that kind of information (e.g. scene artefacts under varying illumination conditions). One promising approach for dealing with those limitations is using images acquired beyond the visible spectrum. This paper reviews some of the existing human body imaging technologies working beyond the visible spectrum (X-ray, Infrared, Millimeter and Submillimeter Wave imaging technologies). The benefits and drawbacks of each technology and their biometric applications are presented.This work has been supported by Terasense (CSD2008-00068) Consolider project of the Spanish Ministry of Science and Technology

Physiology-based face recognition in the thermal infrared spectrum

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e. g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images ( center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area

Biofeedback arrests sympathetic and behavioral effects in distracted driving

Operating machinery while distracted is a dangerous behavior, often habitual, which is the source of accidents. Distracted driving in particular has assumed the form of an epidemic, fueled by the ubiquity of smartphone usage and the tendency to slip into absent-mindedness in tedious commutes. Here we show that a method capable of detecting and communicating overarousal trends associated with the onset of distractions, can pull the driver out of a downward psychophysiological spiral. The method is reliable, unobtrusive, and subtle in its intervention. Arousal estimation is performed by a conservative statistical filter acting upon the driver's perinasal perspiration signal, as this is continuously extracted from a thermal imaging feed. Overarousal notices are communicated via a visual indicator placed in the driver's peripheral vision. Using this method, we conducted a parallel group experiment, where a control $CL (n=23)$ and a biofeedback $BF (n=24)$ cohort were distracted mentally and physically while driving, with only the biofeedback group receiving the benefit of overarousal notification. Results show that heeding biofeedback notices, cuts dramatically the time BF subjects are engaged in distractions with respect to the control group, significantly reducing their arousal levels and improving their driving behaviors in the context of a typical commute