Ioannis A. Kakadiaris

Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality

Detection of Luminal-Intimal Border and Coronary Wall Enhancement in Intravascular Ultrasound Imaging After Injection of Microbubbles and Simultaneous Sonication With Transthoracic Echocardiography

A 61-year-old man presented with unstable angina (Braunwald class 2B). Coronary angiography revealed a mild lesion on the very proximal segment of the left anterior descending coronary artery (LAD) and a significant stenosis (80%) in the mid-segment. Intracoronary ultrasound was used to further evaluate proximal coronary artery stenosis. It was found to be a soft plaque without significant luminal stenosis but without clear definition of the luminal-intimal boundary. Intravenous injection of gas-filled microbubble ultrasound contrast agents have been used for endocardial border detection, especially when they are sonicated by acoustic power and …

Mortality Incidence and the Severity of Coronary Atherosclerosis Assessed by Computed Tomography Angiography

OBJECTIVES This study investigated whether cardiac computed tomography angiography (CTA) can predict all-cause mortality in symptomatic patients. BACKGROUND Noninvasive coronary angiography is being increasingly performed by CTA to assess for obstructive coronary artery disease (CAD), and minimal outcome data exist for coronary CTA. We have utilized a cohort of symptomatic patients who underwent electron beam tomography to allow for longer follow-up (up to 12 years) than currently available with newer 64-slice multidetector-row computed tomography studies. METHODS In all, 2,538 consecutive patients who underwent CTA by electron beam tomography (age 59 +/- 14 years, 70% males) without known CAD were studied. Computed tomographic angiography results were categorized as significant CAD (> or =50% luminal narrowing), mild CAD (<50% stenosis), and normal coronary arteries. Multivariable Cox proportional hazards models were developed to predict all-cause mortality. Risk-adjusted models incorporated traditional risk factors for coronary disease and coronary artery calcification (CAC). RESULTS During a mean follow-up of 78 +/- 12 months, the death rate was 3.4% (86 deaths). The CTA-diagnosed CAD was an independent predictor of mortality in a multivariable model adjusted for age, gender, cardiac risk factors, and CAC (p < 0.0001). The addition of CAC to CTA-diagnosed CAD increased the concordance index significantly (0.69 for risk factors, 0.83 for the CTA-diagnosed CAD, and 0.89 for the addition of CAC to CAD, p < 0.0001). Risk-adjusted hazard ratios for CTA-diagnosed CAD were 1.7-, 1.8-, 2.3-, and 2.6-fold for 3-vessel nonobstructive, 1-vessel obstructive, 2-vessel obstructive, and 3-vessel obstructive CAD, respectively (p < 0.0001), when compared with the group who did not have CAD. CONCLUSIONS The primary results of our study reveal that the burden of angiographic disease detected by CTA provides both independent and incremental value in predicting all-cause mortality in symptomatic patients independent of age, gender, conventional risk factors, and CAC.

3D human body model acquisition from multiple views

We present a novel motion-based approach for the part determination and shape estimation of a humans body parts. The novelty of the technique is that neither a prior model of the human body is employed nor prior body part segmentation is assumed. We present a human body part identification strategy (HBPIS) that recovers all the body parts of a moving human based on the spatiotemporal analysis of its deforming silhouette. We formalize the process of simultaneous part determination and 2D shape estimation by employing the supervisory control theory of discrete event systems. In addition, in order to acquire the 3D shape of the body parts, we present a new algorithm which selectively integrates the (segmented by the HBPIS) apparent contours, from three mutually orthogonal views. The effectiveness of the approach is demonstrated through a series of experiments, where a subject performs a set of movements according to a protocol that reveals the structure of the human body.<<ETX>>

Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition

The uncontrolled conditions of real-world biometric applications pose a great challenge to any face recognition approach. The unconstrained acquisition of data from uncooperative subjects may result in facial scans with significant pose variations along the yaw axis. Such pose variations can cause extensive occlusions, resulting in missing data. In this paper, a novel 3D face recognition method is proposed that uses facial symmetry to handle pose variations. It employs an automatic landmark detector that estimates pose and detects occluded areas for each facial scan. Subsequently, an Annotated Face Model is registered and fitted to the scan. During fitting, facial symmetry is used to overcome the challenges of missing data. The result is a pose invariant geometry image. Unlike existing methods that require frontal scans, the proposed method performs comparisons among interpose scans using a wavelet-based biometric signature. It is suitable for real-world applications as it only requires half of the face to be visible to the sensor. The proposed method was evaluated using databases from the University of Notre Dame and the University of Houston that, to the best of our knowledge, include the most challenging pose variations publicly available. The average rank-one recognition rate of the proposed method in these databases was 83.7 percent.

Evaluation of 3D Face Recognition in the presence of facial expressions: an Annotated Deformable Model approach

From a user’s perspective, face recognition is one of the most desirable biometrics, due to its non-intrusive nature; however, variables such as face expression tend to severely affect recognition rates. We have applied to this problem our previous work on elastically adaptive deformable models to obtain parametric representations of the geometry of selected localized face areas using an annotated face model. We then use wavelet analysis to extract a compact biometric signature, thus allowing us to perform rapid comparisons on either a global or a per area basis. To evaluate the performance of our algorithm, we have conducted experiments using data from the Face Recognition Grand Challenge data corpus, the largest and most established data corpus for face recognition currently available. Our results indicate that our algorithm exhibits high levels of accuracy and robustness, and is not gender biased. In addition, it is minimally affected by facial expressions.

Three-Dimensional Human Body Model Acquisition from Multiple Views

We present a novel approach to the three-dimensional human body model acquisition from three mutually orthogonal views. Our technique is based on the spatiotemporal analysis of the deforming apparent contour of a human moving according to a protocol of movements. For generality and robustness our technique does not use a prior model of the human body and a prior body part segmentation is not assumed. Therefore, our technique applies to humans of any anthropometric dimension. To parameterize and segment over time a deforming apparent contour, we introduce a new shape representation technique based on primitive composition. The composed deformable model allows us to represent large local deformations and their evolution in a compact and intuitive way. In addition, this representation allows us to hypothesize an underlying part structure and test this hypothesis against the relative motion (due to forces exerted from the image data) of the defining primitives of the composed model. Furthermore, we develop a Human Body Part Decomposition Algorithm (HBPDA) that recovers all the body parts of a subject by monitoring the changes over time to the shape of the deforming silhouette. In addition, we modularize the process of simultaneous two-dimensional part determination and shape estimation by employing the Supervisory Control Theory of Discrete Event Systems. Finally, we present a novel algorithm which selectively integrates the (segmented by the HBPDA) apparent contours from three mutually orthogonal viewpoints to obtain a three-dimensional model of the subjects body parts. The effectiveness of the approach is demonstrated through a series of experiments where a subject performs a set of movements according to a protocol that reveals the structure of the human body.

Automated left ventricular segmentation in cardiac MRI

We present an automated left ventricular (LV) myocardial boundary extraction method. Automatic localization of the LV is achieved using a motion map and an expectation maximization algorithm. The myocardial region is then segmented using an intensity-based fuzzy affinity map and the myocardial contours are extracted by cost minimization through a dynamic programming approach. The results from the automated algorithm compared against the experienced radiologists using Bland and Altman analysis were found to have consistent mean bias of 7% and limits of agreement comparable to the inter-observer variability inherent in the manual method.

Computer-aided Non-contrast CT-based Quantification of Pericardial and Thoracic Fat and Their Associations with Coronary Calcium and Metabolic Syndrome

INTRODUCTION Pericardial fat is emerging as an important parameter for cardiovascular risk stratification. We extended previously developed quantitation of thoracic fat volume (TFV) from non-contrast coronary calcium (CC) CT scans to also quantify pericardial fat volume (PFV) and investigated the associations of PFV and TFV with CC and the Metabolic Syndrome (METS). METHODS TFV is quantified automatically from user-defined range of CT slices covering the heart. Pericardial fat contours are generated by spline interpolation between 5-7 control points, placed manually on the pericardium within this cardiac range. Contiguous fat voxels within the pericardium are identified as pericardial fat. PFV and TFV were measured from non-contrast CT for 201 patients. In 105 patients, abdominal visceral fat area (VFA) was measured from an additional single-slice CT. In 26 patients, images were quantified by two readers to establish inter-observer variability. TFV and PFV were examined in relation to Body Mass Index (BMI), waist circumference and VFA, standard coronary risk factors (RF), CC (Agatston score >0) and METS. RESULTS PFV and TFV showed excellent correlation with VFA (R=0.79, R=0.89, p<0.0001), and moderate correlation with BMI (R=0.49, R=0.48, p<0.0001). In 26 scans, the inter-observer variability was greater for PFV (8.0+/-5.3%) than for TFV (4.4+/-3.9%, p=0.001). PFV and TFV, but not RF, were associated with CC [PFV: p=0.04, Odds Ratio 3.1; TFV: p<0.001, OR 7.9]. PFV and TFV were also associated with METS [PFV: p<0.001, OR 6.1; TFV p<0.001, OR 5.7], unlike CC [OR=1.0 p=NS] or RF. PFV correlated with low-HDL and high-glucose; TFV correlated with low-HDL, low-adiponectin, and high glucose and triglyceride levels. CONCLUSIONS PFV and TFV can be obtained easily and reproducibly from routine CC scoring scans, and may be important for risk stratification and monitoring.

Estimating anthropometry and pose from a single image

In this paper, we present a four-step technique for simultaneously estimating a humans anthropometric measurements (up to a scale parameter) and pose from a single image. The user initially selects a set of image points that constitute the projection of selected landmark. Using this information, along with a priori statistical information about the human body, a set of plausible segment length estimates are generated. The third step produces a set of plausible poses based on joint limit constraints using a geometric method. In the fourth step, pose and anthropometric measurements are obtained by minimizing an appropriate cost function subject to the associated constraints. The novelty of our approach is the use of anthropometric statistics to constrain the estimation process that allows the simultaneous estimation of both anthropometry and pose. We demonstrate the accuracy, advantages and limitations of our method for various classes of both synthetic and real input data.