Pradeep Buddharaju

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

NEAT-o-Games: blending physical activity and fun in the daily routine

This article describes research that aims to encourage physical activity through a novel pervasive gaming paradigm. Data from a wearable accelerometer are logged wirelessly to a cell phone and control the animation of an avatar that represents the player in a virtual race game with other players over the cellular network. Winners are declared every day and players with an excess of activity points can spend some to get hints in mental games of the suite, like Sudoku. The racing game runs in the background throughout the day and every little move counts. As the gaming platform is embedded in the daily routine of players, it may act as a strong behavioral modifier and increase everyday physical activity other than volitional sporting exercise. Such physical activity (e.g., taking the stairs), is termed NEAT and was shown to play a major role in obesity prevention and intervention. A pilot experiment demonstrates that players are engaged in NEAT-o-Games and become more physically active while having a good dosage of fun.

Face Recognition in the Thermal Infrared Spectrum

We present a two-stage face recognition method based on infrared imaging and statistical modeling. In the first stage we reduce the search space by finding highly likely candidates before arriving at a singular conclusion during the second stage. Previous work has shown that Bessel forms model accurately the marginal densities of filtered components and can be used to find likely matches but not a unique solution. We present an enhancement to this approach by applying Bessel modeling on the facial region only rather than the entire image and by pipelining a classification algorithm to produce a unique solution. The detailed steps of our method are as follows: First, the faces are separated from the background using adaptive fuzzy connectedness segmentation. Second, Gabor filtering is used as a spectral analysis tool. Third, the derivative filtered images are modeled using two-parameter Bessel forms. Fourth, high probability subjects are short-listed by applying the L^2 -norm on the Bessel models. Finally, the resulting set of highly likely matches is fed to a Bayesian classifier to find the exact match. We show experimentally that segmentation of the facial regions results in better hypothesis pruning and classification performance. We also present comparative experimental results with an eigenface approach to highlight the potential of our method.

Fast by Nature - How Stress Patterns Define Human Experience and Performance in Dexterous Tasks

In the present study we quantify stress by measuring transient perspiratory responses on the perinasal area through thermal imaging. These responses prove to be sympathetically driven and hence, a likely indicator of stress processes in the brain. Armed with the unobtrusive measurement methodology we developed, we were able to monitor stress responses in the context of surgical training, the quintessence of human dexterity. We show that in dexterous tasking under critical conditions, novices attempt to perform a tasks step equally fast with experienced individuals. We further show that while fast behavior in experienced individuals is afforded by skill, fast behavior in novices is likely instigated by high stress levels, at the expense of accuracy. Humans avoid adjusting speed to skill and rather grow their skill to a predetermined speed level, likely defined by neurophysiological latency.

Pose-Invariant Physiological Face Recognition in the Thermal Infrared Spectrum

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 (TMP) 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 (5) different pose images (center, mid-left profile, left profile, mid-right 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 sizeable database of thermal facial images collected in our lab. The good experimental results show that the proposed methodology has merit. More important, 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.

Physiology-based face recognition

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.

Face Recognition Beyond the Visible Spectrum

The facial vascular network is highly characteristic to the individual, much like the way his fingerprint is. A non-obtrusive way to capture this information is through thermal imaging. The convective heat transfer effect from the flow of “hot” arterial blood in superficial vessels creates characteristic thermal imprints, which are at a gradient with the surrounding tissue. This casts sigmoid edges on the human tissue where major blood vessels are present. We present an algorithmic methodology to extract and represent the facial vasculature. The methodology combines image morphology and probabilistic inference. The morphology captures the overall structure of the vascular network while the probabilistic part reflects the positional uncertainty for the vessel walls, due to the phenomenon of thermal diffusion. The accuracy of the methodology is tested through extensive experimentation and meticulous ground-truthing. Furthermore, the efficacy of this information for identity recognition is tested on substantial databases.

Multispectral Face Recognition: Fusion of Visual Imagery with Physiological Information

We present a novel multi-spectral approach for face recognition using visual imagery as well as the physiological information extracted from thermal facial imagery. The main point of this line of research is that physiological information available only in thermal infrared, can improve the performance and enhance the capabilities of standard visual face recognition methods. For each subject in the database, we store facial images collected simultaneously in the visual and thermal bands. For each of the thermal images, we first delineate the human face from the background using the Bayesian framework. We then extract the blood vessels present on the segmented facial tissue using image morphology. The extracted vascular network produces contour shapes that are unique to each individual. The branching points of the skeletonized vascular network, referred to as thermal minutia points (TMPs), are an effective feature abstraction. During the classification stage, we match the local and global structures of TMPs extracted from the test image with those of the corresponding images in the database. We fuse the recognition results of our thermal imaging algorithm with those of a popular visual imaging algorithm. We have conducted experiments on a large database of co-registered visual and thermal facial images. The good experimental results show that the proposed fusion approach has merit and promise.

Automatic thermal monitoring system (ATHEMOS) for deception detection

Previous work has demonstrated the correlation of periorbital perfusion and stress levels in human beings. In this paper, we report results on a large and realistic mock-crime interrogation experiment. The interrogation is free flowing and no restrictions have been placed on the subjects. We propose a new methodology to compute the average periorbital temperature signal. The present approach addresses the deficiencies of the earlier methodology and is capable of coping with the challenges posed by the realistic setting. Specifically, it features a tandem condensation tracker to register the periorbital area in the context of a moving face. It operates on the raw temperature signal and tries to improve the information content by suppressing the noise level instead of amplifying the signal as a whole. Finally, a pattern recognition method classifies stressful (deceptive) from non-stressful (non-deceptive) subjects based on a comparative measure between the interrogation signal (baseline) and portions thereof (transient response).

Physiological face recognition is coming of age

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.