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-all important characteristics for real-time corrections on human handling of critical machinery. 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 CLCL (n=23n=23) and a biofeedback BFBF (n=24n=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 BFBF 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.

Workplace environments are characterized by frequent interruptions that can lead to stress. However, measures of stress due to interruptions are typically obtained through self-reports, which can be affected by memory and emotional biases. In this paper, we use a thermal imaging system to obtain objective measures of stress and investigate personality differences in contexts of high and low interruptions. Since a major source of workplace interruptions is email, we studied 63 participants while multitasking in a controlled office environment with two different email contexts: managing email in batch mode or with frequent interruptions. We discovered that people who score high in Neuroticism are significantly more stressed in batching environments than those low in Neuroticism. People who are more stressed finish emails faster. Last, using Linguistic Inquiry Word Count on the email text, we find that higher stressed people in multitasking environments use more anger in their emails. These findings help to disambiguate prior conflicting results on email batching and stress.

The previous work of the authors has shown that physiological information on the face can be extracted from thermal infrared imagery and can be used as a biometric. Although, that work has proved the feasibility of physiological face recognition, the experimental results revealed high false acceptance rates due to methodological weaknesses in the feature extraction and matching algorithms. This paper, presents a new methodology that corrects these problems and yields high recognition rates. Specifically, a post-processing algorithm removes fake vascular contours, which degraded performance. Also, a new vascular network matching algorithm copes with deformations caused by varying facial pose and expressions. First, it estimates the facial pose in the test image and then calculates the deformation of the vascular network in the database image. Next, it registers test and database vascular networks using the dual bootstrap iterative closest point (ICP) matching algorithm. Finally, it computes a matching score between the vascular networks, which is a function of overlapping vessel pixels. Extensive experiments have been undertaken to test the new method. The results highlight its superiority.

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.

Applying advanced video technology to understand (human) activity and intent, including the interaction of multiple people and objects, is becoming increasingly important, especially for intelligent video surveillance. Recently, technical interest in video surveillance has moved from lowlevel processing modules, such as motion detection and motion tracking, to activity awareness and more complex scene understanding. This paper presents an integrated video surveillance system at Honeywell labs, which detects predefined activities with improved robustness. Also, we present the ‘new activity’ detection (pattern discovery), which can automatically capture new activities, and present the newly detected activities to the operator who checks for their validity and adds them into the activity models. Moreover, we present a torso angle feature, which represents people posture, to detect activities, such as people falling. We used real world data sets to show the effectiveness of our proposed method.

We present a novel approach for face recognition based on the physiological information extracted from thermal facial images. First, we delineate the human face from the background using a Bayesian method. Then, we extract the blood vessels present on the segmented facial tissue using image morphology. The extracted vascular network produces contour shapes that are unique for each individual. The branching points of the skeletonized vascular network are referred to as thermal minutia points (TMPs). These are reminiscent of the minutia points produced in fingerprint recognition techniques. During the classification stage, local and global structures of TMPs extracted from test images are matched with those of database images. We have conducted experiments on a large database of thermal facial images collected in our lab. The good experimental results show that our proposed approach has merit and promise.

An artificial vision system is presented for lie detection by analyzing face thermal image sequences.This system represents an alternative technique to the polygraph.Some of its features are: 1) it has no physical contact with the examinee, 2) it is non-intrusive, 3) it has a potential for private use, and 4) it can simultaneously analyze several persons.The proposed system is based on the detection of physiological changes in temperature in the lacrimal puncta area caused by the subtle increase in blood flow through the nearby vascular network.These changes take place when anxiety appears as a consequence of deception.Thus, the system segments the periorbital area, and tracks consecutive frames using the Kanade-Lucas-Tomasi algorithm.The results show a success rate of 79.2 % in detecting lies using a simple classification based on the comparison between the estimated temperatures in control questions, and the rest of the interrogation procedure.The performance of this system is comparable with previous works, where cameras with better specifications were used.