Parenting while serving as a frontline leader is uniquely stressful, yet little is known about how family responsibilities shape physiological stress in these roles. We followed emergency physicians and tactical police leaders, comparing parents of young children with non-parents across four days: one critical mission day, two standard workdays, and one non-workday. Using wearable sensing, expert activity labeling, and daily debriefs, we inferred stress only in sedentary epochs via a normalized-heart-rate method, with an HRV-based index as benchmark. Parents showed higher stress on workdays and non-workdays, but not on critical mission days, where attentional narrowing and strict device policies appear to suppress parenting-related differences. We contribute: (i) in-the-wild physiological evidence that parenthood amplifies stress mainly under permeable boundaries, (ii) a pragmatic stress-labeling pipeline for safety-critical settings, (iii) a configuration-based account linking boundaries, attention, and parenting, and (iv) design implications for stress-aware boundary management systems, supported by an open analysis repository.

We investigate mental stress and relaxation patterns in professionals occupying leadership roles in emergency care and special police units. A key finding is that on days that involve critical missions, these individuals experience negative mental stress (i.e., distress) that escalates as the day unfolds. In contrast, on non-leadership workdays, mental distress remains relatively stable, while on non-workdays, participants exhibit positive mental stress (i.e., eustress) that subsides over time, facilitating relaxation. These findings stem from a four-day naturalistic study of$n=24$professionals, during which we collected physiological, mobility, and psychometric data. Using participant debriefings and sensor-based validation triggers (e.g., GPS, cadence), we labeled activities in 5-minute intervals and focused our analysis on sedentary periods. We defined stress during these periods as a normalized heart rate that exceeds two standard deviations above a personalized baseline, thus isolating mental stress uncontaminated by physical exertion. A logistic regression model based on this stress labeling method yielded results largely consistent with those obtained from Kubios' SNS Index, reinforcing its validity. In cases of disagreement, our method aligned better with participant reports and established literature, highlighting advantages in interpretability and specificity. Overall, our work makes three contributions: (a) to affective science, by quantifying the mentally stressful nature of leadership in high-stakes environments; (b) to affective computing, by proposing a wearable compatible method for estimating mental stress during sedentary activity in the wild; (c) to data science, by introducing a well-annotated, multimodal dataset suitable for machine learning benchmarking in stress detection.

In affective computing, breathing has seen lighter use than the heart and EDA channels. Several reasons have contributed to this, including difficulties in disambiguating affective from speech effects and perceived lack of generalizability. Here we report a framework that addresses these issues. The cornerstone of the framework is a comprehensive set of physiologically informed features, comprised of three groups: breathing depth, respiratory time quotient (RTQ), and breathing speed features. The breathing depth features capture either mental arousal or fear effects. The RTQ features capture speech production. The breathing speed features capture arousal effects due to emotional influences. The said framework appears to have broad applicability. In the naturalisticOffice Tasks 2019dataset with speaking sessions, the said features used either in regression or random forest models led to robust classification of arousal ($\overline{\text{AUC}}$in [0.75, 0.96]) stemming from three different conditions: a) mental-emotional stressor effected through a time-pressured knowledge task; b) pure mental stressor effected through a long knowledge task; c) mental-social stressor effected through a public speech task. In the stylizedCASEdataset with silent sessions, the same features and algorithms led to solid classification of arousal ($\overline{\text{AUC}}$in [0.71, 0.85]) stemming from scary vs. non-scary movie clips.

We report on a naturalistic study investigating the effects of routine driving on cardiovascular activation. We recruited 21 healthy young adults from a broad geographic area in the Southwestern United States. Using the participants' own smartphones and smartwatches, we monitored for a week both their driving and non-driving activities. Monitoring included the continuous recording of a) heart rate throughout the day, b) hand motion during driving as a proxy of persistent texting, and c) contextualized driving data, complete with traffic and weather information. These high temporal resolution variables were complemented with the drivers' biographic and psychometric profiles. Our analysis suggests that anxiety predisposition and high speeds are associated with significant cardiovascular activation on drivers, likely linked to sympathetic arousal. Surprisingly, these associations hold true under good weather, normal traffic, and with experienced drivers behind the wheel. The said findings call for attention to insidious effects of apparently benign drives even for people in their prime. Accordingly, our research contributes to intriguing new discourses on driving affect and personal health informatics.

In The formative years of Affective Computing [1], from the late 1990s and into the early 2000s, a significant fraction of research attention was focused on the development of methods forunobtrusive physiological measurement. It quickly became obvious that wiring people with electrodes and strapping cumbersome hardware to their bodies was not only restricting the types of experiments that could be performed but also was not conducive to unbiased observations. For instance, subjects with fingers wrapped with electrodermal activity (EDA) and photoplethysmography (PPG) sensors could hardly type, drive or sleep comfortably. Hence, there was a need for more elegant and scalable physiological measurement methods [2].

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.

Working in an environment with constant interruptions is known to affect stress, but how do interruptions affect emotional expression? Emotional expression can have significant impact on interactions among coworkers. We analyzed the video of 26 participants who performed an essay task in a laboratory while receiving either continual email interruptions or receiving a single batch of email. Facial videos of the participants were run through a convolutional neural network to determine the emotional mix via decoding of facial expressions. Using a novel co-occurrence matrix analysis, we showed that with batched email, a neutral emotional state is dominant with sadness being a distant second, and with continual interruptions, this pattern is reversed, and sadness is mixed with fear. We discuss the implications of these results for how interruptions can impact employees' well-being and organizational climate.

Both distracted and aggressive driving are habitual in nature, constituting an insurance risk, which has been difficult to quantify. Here, in this paper, we propose a method that produces short term predictions for these two dangerous driving behaviors. The method feeds an Extreme Gradient Boosting (XGB) algorithm with the most informative features of a set of physiological and vehicular variables. The XGB algorithm operates on a learning window covering the last 30 seconds to make fast track predictions (FT) for the next 10 seconds. For aggressive driving, FT predictions are final, while for distracted driving, FT predictions are weighted over one minute, to form a meta-prediction. This more deliberative process for predicting distractions fits their intermittent manifestation. The method has been tested on SIM 1, a publicly available dataset from a distracted driving experiment. In this dataset, the drivers ($n=59$) are labeled as distracted based on the presence of mental activity or physical interactions antagonistic to the driving task; their driving style is defined by steering and acceleration, and is classified as aggressive or normal. The method attains classification performance that exceeds 87%. Alerting drivers when distractions and aggressiveness have taken hold on them can provide sobering awareness, given that people drift into these states subconsciously. The behavioral modification effects of such awareness mechanisms are rooted in Cognitive Behavioral Theory. The proposed method can also be used in future vehicles with advanced automation, weighing in the computer’s decision to wrest vehicular control from an unrepentant driver.

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 papers in this special section focus on neuro-informatics which is considered one of the most attractive research fields for scientists, engineers, practitioners and physicians due to its profound importance in healthcare and in our lives. Human curiosity, the BRAIN project in USA with a very large funding budget, and the exponential evolution of computational informatics and nanotech during the last two decades have inspired and motivated many researchers around the globe to contribute with their research to the brain. The papers are associated with the IEEE BIBE-2017 Conference.

This article documents thermophysiological patterns associated with migraine episodes, where the inner canthi and supraorbital temperatures drop significantly compared to normal conditions. These temperature drops are likely due to vasoconstriction of the ophthalmic arteries under the inner canthi and sympathetic activation of the eccrine glands in the supraorbital region, respectively. The thermal patterns were observed on eight migraine patients and meticulously quantified using advance computational methods, capable of delineating small anatomical structures in thermal imagery and tracking them automatically over time. These methods open the way for monitoring migraine episodes in nonclinical environments, where the patient maintains directional attention, such as his/her computer at home or at work. This development has the potential to significantly expand the operational envelope of migraine studies.

Waveforms extracted via nasal thermistors are the most common signals used to study breathing function in sleep studies. In recent years, unobtrusive alternatives have been developed based on thermal imaging. Initially, the research aimed to produce a measurement on par with the clinical standard (the thermistor), but at a distance. Lately, there has been recognition that imaging is inherently multidimensional and can produce spatiotemporal and not just temporal signals - a development with significant diagnostic value. The extraction of 3D breathing information, however, has been based on inaccurate assumptions regarding the formation of the nasal thermal patterns sensed by the camera. The present paper corrects these assumptions, enabling the production of more accurate and complete multidimensional breathing signals.

More than 70 million people have been infected with the human immunodeficiency virus (HIV). There is no cure for HIV and modern treatment is only effective at delaying the onset of acquired immunodeficiency syndrome. Incorporated HIV serves as a reservoir for constant release of virions. Knowing the locations and quantity of HIV in the reservoirs can help guide development of complete treatment. Patient/Organ-specific HIV genome reconstruction allows to significantly improve detection of sequences originating from HIV. In this paper, we present a novel personalized medicine approach based on the reconstruction of patient/organ-specific HIV genome sequences in latent reservoirs.

Electrodermal activity (EDA) is an important affective indicator, measured conventionally on the fingers with desktop sensing instruments. Recently, a new generation of wearable, battery-powered EDA devices came into being, encouraging the migration of EDA sensing to other body locations. To investigate the implications of such sensor/location shifts in psychophysiological studies we performed a validation experiment. In this experiment we used startle stimuli to instantaneously arouse the sympathetic system of n=23 subjects while sitting. Startle stimuli are standard but minimal stressors, and thus ideal for determining the sensor and location resolution limit. The experiment revealed that precise measurement of small EDA responses on the fingers and palm is feasible either with conventional or mobile EDA sensors. By contrast, precise measurement of small EDA responses on the sole is challenging, while on the wrist even detection of such responses is problematic for both EDA modalities. Given that affective wristbands have emerged as the dominant form of EDA sensing, researchers should beware of these limitations.

This study explored various user interface designs to transition a two dimensional (2D) questionnaire from its paper-and-pencil testing format to the mobile platform. The current administration of the test limits its usage beyond the lab environment. Creating a mobile version would facilitate ubiquitous administration of the test. Yet, the mobile design must be at least as good as its paper-based counterpart in terms of input accuracy and user interaction efforts. We developed four user interface designs, each of which featured a specific interaction approach. These approaches included displaying the 2D space of the questionnaire in its original form (M1), inputting one variable at a time on the 2D space (M2), dissolving the 2D space into two one-dimensional ordinal scales (M3), and orienting the input selections to the diagonal axes (M4). The designs were tested by a total of 34 participants, aged 18 to 52 years. The study results find the first three interaction approaches (M1-M3) effective but the fourth approach inefficient. Furthermore, the results indicate that the two-tap designs (M2 and M3) are equally as good as the one-tap design (M1).

In this paper, we present a novel framework for quantifying physiological stress at a distance via thermal imaging. The method captures stress-induced neurophysiological responses on the perinasal area that manifest as transient perspiration. We have developed two algorithms to extract the perspiratory signals from the thermophysiological imagery. One is based on morphology and is computationally efficient, while the other is based on spatial isotropic wavelets and is flexible; both require the support of a reliable facial tracker. We validated the two algorithms against the clinical standard in a controlled lab experiment where orienting responses were invoked on n=18 subjects via auditory stimuli. Then, we used the validated algorithms to quantify stress of surgeons (n=24) as they were performing suturing drills during inanimate laparoscopic training. This is a field application where the new methodology shines. It allows nonobtrusive monitoring of individuals who are naturally challenged with a task that is localized in space and requires directional attention. Both algorithms associate high stress levels with novice surgeons, while low stress levels are associated with experienced surgeons, raising the possibility for an affective measure (stress) to assist in efficacy determination. It is a clear indication of the methodology's promise and potential.

Unlike machines, we humans are prone to boredom when we perform routine activities for long periods of time. Workers' mental engagement in boring tasks diminishes, which eventually, compromises their performance. The result is a double-whammy because the workers do not get job satisfaction and their employers do not receive optimal return on investment. This paper proposes a novel way for improving workers' mental engagement and hence, enjoyment, in routine activities. Specifically, we propose to blend in routine tasks mild mental/physical challenges. To test our hypothesis, we chose to experiment on a monitoring task typical of security guard operations. We combined this routine task with an iPhone-based game to make it more enjoyable. The results from 10 participants show that their mental engagement and enjoyment were significantly higher during the combined task.

User intervention in the periorbital thermal signal extraction process breaks down automation. This paper proposes a novel way to minimize user intervention. While previous work demonstrated the importance of accurate computation of the periorbital signal, the present method enables its automatic extraction at a reduced processing time. The proposed algorithm capitalizes on detection of involuntary eye blinking in the thermal imagery. The need for automation has emerged because of repetitive processing of the same subjects, aiming to validate improvements in the periorbital tissue tracking or segmentation algorithms. The proposed approach initiates the tracking and segmentation algorithms on the same spatio-temporal location in repetitive runs of the thermal clip. Thus, it does not only automate the process but also eliminates the variability introduced by manual intervention. We have tested the algorithm on thermal video clips of 39 subjects who faced stressful interrogation for a mock crime. The results show that the proposed method has reduced total processing time from a week down to a day.

One of the major challenges of video game design is to have appropriate difficulty levels for users in order to maximize the entertainment value of the game. Game players may lose interests if a game is either too easy or too difficult. This paper presents a novel methodology to improve user's experience in computer games by automatically adjusting the level of the game difficulty. The difficulty level is computed from measurements of the facial physiology of the players at a distance. The measurements are based on the assumption that the players' performance during the game-playing session alters blood flow in the supraorbital region, which is an indirect measurement of increased mental activities. This alters heat dissipation, which can be monitored in a contact-free manner through a thermal imaging-based stress monitoring and analysis system, known as StressCam.

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.

A novel template update method is proposed for facial tissue tracking in thermal clips. It is a smoothed matte approach, which provides the location along with the rate of updating. The template is able to adapt to abrupt orientation and physiological changes, while remaining robust to noise disturbances. Furthermore, it addresses successfully the difficult template drift problem. The new method was tested on tracking face regions in 40 thermal clips of individuals in varying psycho-physiological and environmental conditions. It demonstrated stability and accuracy, outperforming other template update strategies. The method promises improved performance in contact-free polygraphy and thus, it is of value in this emerging form of close range surveillance.

Thermal imaging techniques have been applied to detect and measure mental stress in polygraph screening and other applications. Mental stress is highly correlated with the activation of the corrugator muscle on the forehead. The vessels that supply blood to the corrugator muscle, proportionally to its degree of activation, are the supraorbital vessels. The rate of blood flow in these vessels can be indirectly measured via the intensity of heat emission from their segments. However, segmenting the thermal imprints of the supraorbital vessels is challenging because (1) they are fuzzy due to thermal diffusion, and (2) exhibit significant inter-individual and intra-individual variation. In this paper, a new segmentation method is proposed to extract the supraorbital vessels in thermal imagery. The new method features three steps: (1) automatic initialization of vessels; (2) automatic localization of the central lines of vessels; and (3) fast determination of vessel boundaries. The results show that the new method achieves high quality segmentation in both a simulated and a real dataset. The proposed method is expected to further increase the accuracy of stress measurements via thermal imaging.

The proposed demonstration is based on the work performed as part of the NEAT-o-Games project. NEAT-o-Games is a suite of games that runs on mobile terminals such as cell phones. Unlike other games, NEAT-o-Games' primary goal is to become part of people's everyday routines and attack the behavioral aspect of the sedentary lifestyle. Their main characteristic is that they are not carried out in short bouts, but are being played continuously and are interwoven in the daily routine of the players. Data from wearable accelerometers are logged wirelessly to a cell phone and control the animation of the player in a virtual race game (avatar) with other players over the cellular network. Players can use their excess of activity points earned from the race game to get hints in mental games of the suite, like Sudoku. Initial studies indicate that NEAT-o-Games may bring a positive physical, psychological, and social impact on players.

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.

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.

In this paper, we propose a novel tracker to capture the human breathing signal through an infrared imaging method. Human facial physiology information is used to select salient thermal features on the human face as good features to track. The major component of the tracker is mean shift localization (MSL)-based particle filtering. A special measurement model is designed for particle filtering so that the tracker can handle significant head movement and object occlusion. The breathing signal is achieved based on tracking results. The experiments show that the tracker is robust and stable and the recovered breathing signal is clear enough for breathing functionality computation.

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.

We have developed a novel method to measure human cardiac pulse at a distance. It is based on the information contained in the thermal signal emitted from major superficial vessels. This signal is acquired through a highly sensitive thermal imaging system. Temperature on the vessel is modulated by pulsative blood flow. To compute the frequency of modulation (pulse), we extract a line-based region along the vessel. Then, we apply Fast Fourier Transform (FFT) to individual points along this line of interest to capitalize on the pulse propagation effect. Finally, we use an adaptive estimation function on the average FFT outcome to quantify the pulse. We have tested the accuracy of our method on 5 subjects with highly successful results. The technology is expected to find applications among others in sustained physiological monitoring of cardiopulmonary diseases, sport training, sleep studies, and psychophysiology (polygraph).

We propose a layered security concept for the protection of buildings' air-intakes from chemical and biological attacks. The concept is focused on prevention, early warning, and effective evacuation. One of the pillars of our concept is the inclusion of a video-based surveillance subsystem that improves the early warning capability of the protection system. Our video-based surveillance subsystem features a motion detection and tracking module based on DETER - an algorithm that we described previously. We have tested our video surveillance system on various possible attack scenarios with perfect detection results. The current weak point of our solution is that it cannot differentiate between benign and malicious human activities. We are working on incorporating a human activity recognition module, which will endow the video surveillance subsystem with threat assessment capabilities.

We propose a revolutionary concept for detecting suspects engaged in illegal and potentially harmful activities in or around critical military or civilian installations. We investigate the use of thermal image analysis to detect at a distance facial patterns of anxiety, alertness, and/or fearfulness. This is a totally novel approach to the problem of biometric identification. Instead of focusing on the question "who are you" we focus instead on the question "what are you about to do". Documented preliminary results clearly indicate the feasibility of the idea.

Research in the surveillance domain was confined for years in the military domain. Recently, as military spending for this kind of research was reduced and the technology matured, the attention of the research and development community turned to commercial applications of surveillance. In this paper we describe a state-of-the-art monitoring system developed by a corporate R&D lab in cooperation with the corresponding security business units. It represents a sizable effort to transfer some of the best results produced by computer vision research into a viable commercial product. Our description spans both practical and technical issues. From the practical point of view we analyze the state of the commercial security market, typical cultural differences between the research team and the business team and the perspective of the potential users of the technology. These are important issues that have to be dealt with or the surveillance technology will remain in the lab for a long time. From the technical point of view we analyze our algorithmic and implementation choices. We describe the improvements we introduced to the original algorithms reported in the literature in response to some problems that arose during field testing. We also provide extensive experimental results that highlight the strong points and some weaknesses of the prototype system.

We undertook a study to determine if the automatic detection and counting of vehicle occupants is feasible. In the present paper, we report our findings regarding the appropriate sensor phenomenology and arrangement for the task. We propose a novel system based on fusion of near-infrared imaging signals and demonstrate its adequacy with theoretical and experimental arguments. We also propose a fuzzy neural network classifier to operate upon the fused near-infrared imagery and perform the occupant detection and counting function. We demonstrate experimentally that the combination of fused near-infrared phenomenology and fuzzy neural classification produces a robust solution to the problem of automatic vehicle occupant counting. We substantiate our argument by providing comparative experimental results for vehicle occupant counters based on visible, single near-infrared, and fused near-infrared bands. Our proposed solution can find a more general applicability as the basis for a reliable face detector both indoors and outdoors.

We propose a novel user-dependent method for the recognition of on-line handwritten notes. The method employs as a dissimilarity measure the "degree of morphing" between an input curve and a template curve. A physics-based approach substantiates the "degree of morphing" as a deformation energy and casts the problem as an energy minimization problem. The method operates upon key segmentation points that are provided by an appropriate segmentation algorithm. The segmentation objective is not to locate letters, but instead to locate corners and some key low curvature points (an easier task). This is part of the method's strategy to see the word as a generic on-line curve. Due to this strategy, the proposed method can handle collectively both cursive words and hand-drawn line figures, the two key ingredients of handwritten notes. Most importantly, the proposed system achieves high recognition rates without ever resorting to statistical models.

A novel method for 2D shape recognition is proposed. The method employs as a dissimilarity measure the degree of morphing between a test shape and a reference shape. A physics-based approach substantiates the degree of morphing as a deformation energy and casts the problem as an energy minimization problem. The method operates upon key segmentation points that are provided by an appropriate segmentation algorithm. The recognition paradigm is invariant to translation, rotation, and scaling. It can handle both convex and non-convex shapes. The proposed system exhibits robust recognition behavior and real-time performance in a series of experiments. The experiments also highlight the ability of the method to recognize deformable shapes.

A novel method that recognizes on-line handwritten patterns (typical in pen-based computing applications) is proposed. The method combines the advantages of both global and local recognition methods, works in real-time, and avoids the use of statistical models that require extensive user data. It is also one of the first methods that handles collectively cursive words, and hand-drawn line figures. The proposed system achieves pattern recognition through the use of shape metamorphosis. It is based on the premise that if two shapes are similar they don't have to undergo a substantial metamorphosis process in order for one to assume the shape of the other. In other words, the "degree of morphing" becomes the primary matching criterion. The notion of the "degree of morphing" is quantified through an energy minimization approach. The potential of the method is highlighted by a set of experiments.

A novel curve segmentation algorithm for determining control points for deformable-model-based target tracking is proposed. The algorithm is parameterless enabling a fully-fledged automated tracking regardless of the shape of the object being tracked. Compared with other curve segmentation algorithms, it selects a minimal number of control points that yet deliver a superior shape description. The algorithm is comparatively tested with other curve segmentation algorithms in a variety of characteristic target outlines.