Shape-appearance-correlated active appearance model

© 2016 Elsevier Ltd Among the challenges faced by current active shape or appearance models, facial-feature localization in the wild, with occlusion in a novel face image, i.e. in a generic environment, is regarded as one of the most difficult computer-vision tasks. In this paper, we propose an Active Appearance Model (AAM) to tackle the problem of generic environment. Firstly, a fast face-model initialization scheme is proposed, based on the idea that the local appearance of feature points can be accurately approximated with locality constraints. Nearest neighbors, which have similar poses and textures to a test face, are retrieved from a training set for constructing the initial face model. To further improve the fitting of the initial model to the test face, an orthogonal CCA (oCCA) is employed to increase the correlation between shape features and appearance features represented by Principal Component Analysis (PCA). With these two contributions, we propose a novel AAM, namely the shape-appearance-correlated AAM (SAC-AAM), and the optimization is solved by using the recently proposed fast simultaneous inverse compositional (Fast-SIC) algorithm. Experiment results demonstrate a 5–10% improvement on controlled and semi-controlled datasets, and with around 10% improvement on wild face datasets in terms of fitting accuracy compared to other state-of-the-art AAM models

The Tracking Performance of Distributed Recoverable Flight Control Systems Subject to High Intensity Radiated Fields

It is known that high intensity radiated fields (HIRF) can produce upsets in digital electronics, and thereby degrade the performance of digital flight control systems. Such upsets, either from natural or man-made sources, can change data values on digital buses and memory and affect CPU instruction execution. HIRF environments are also known to trigger common-mode faults, affecting nearly-simultaneously multiple fault containment regions, and hence reducing the benefits of n-modular redundancy and other fault-tolerant computing techniques. Thus, it is important to develop models which describe the integration of the embedded digital system, where the control law is implemented, as well as the dynamics of the closed-loop system. In this dissertation, theoretical tools are presented to analyze the relationship between the design choices for a class of distributed recoverable computing platforms and the tracking performance degradation of a digital flight control system implemented on such a platform while operating in a HIRF environment. Specifically, a tractable hybrid performance model is developed for a digital flight control system implemented on a computing platform inspired largely by the NASA family of fault-tolerant, reconfigurable computer architectures known as SPIDER (scalable processor-independent design for enhanced reliability). The focus will be on the SPIDER implementation, which uses the computer communication system known as ROBUS-2 (reliable optical bus). A physical HIRF experiment was conducted at the NASA Langley Research Center in order to validate the theoretical tracking performance degradation predictions for a distributed Boeing 747 flight control system subject to a HIRF environment. An extrapolation of these results for scenarios that could not be physically tested is also presented

Creation of Large Scale Face Dataset Using Single Training Image

Face recognition (FR) has become one of the most successful applications of image analysis and understanding in computer vision. The learning-based model in FR is considered as one of the most favorable problem-solving methods to this issue, which leads to the requirement of large training data sets in order to achieve higher recognition accuracy. However, the availability of only a limited number of face images for training a FR system is always a common problem in practical applications. A new framework to create a face database from a single input image for training purposes is proposed in this dissertation research. The proposed method employs the integration of 3D Morphable Model (3DMM) and Differential Evolution (DE) algorithms. Benefitting from DE\u27s successful performance, 3D face models can be created based on a single 2D image with respect to various illumination and pose contexts. An image deformation technique is also introduced to enhance the quality of synthesized images. The experimental results demonstrate that the proposed method is able to automatically create a virtual 3D face dataset from a single 2D image with high performance. Moreover the new dataset is capable of providing large number of face images equipped with abundant variations. The validation process shows that there is only an insignificant difference between the input image and the 2D face image projected by the 3D model. Research work is progressing to consider a nonlinear manifold learning methodology to embed the synthetically created dataset of an individual so that a test image of the person will be attracted to the respective manifold for accurate recognition

Human Motion Analysis for Efficient Action Recognition

Automatic understanding of human actions is at the core of several application domains, such as content-based indexing, human-computer interaction, surveillance, and sports video analysis. The recent advances in digital platforms and the exponential growth of video and image data have brought an urgent quest for intelligent frameworks to automatically analyze human motion and predict their corresponding action based on visual data and sensor signals. This thesis presents a collection of methods that targets human action recognition using different action modalities. The first method uses the appearance modality and classifies human actions based on heterogeneous global- and local-based features of scene and humanbody appearances. The second method harnesses 2D and 3D articulated human poses and analyizes the body motion using a discriminative combination of the parts’ velocities, locations, and correlations histograms for action recognition. The third method presents an optimal scheme for combining the probabilistic predictions from different action modalities by solving a constrained quadratic optimization problem. In addition to the action classification task, we present a study that compares the utility of different pose variants in motion analysis for human action recognition. In particular, we compare the recognition performance when 2D and 3D poses are used. Finally, we demonstrate the efficiency of our pose-based method for action recognition in spotting and segmenting motion gestures in real time from a continuous stream of an input video for the recognition of the Italian sign gesture language

Discriminative Appearance Models for Face Alignment

The proposed face alignment algorithm uses local gradient features as the appearance representation. These features are obtained by pixel value comparison, which provide robustness against changes in illumination, as well as partial occlusion and local deformation due to the locality. The adopted features are modeled in three discriminative methods, which correspond to different alignment cost functions. The discriminative appearance modeling alleviate the generalization problem to some extent

Evaluation and Hardware Realization for a Face Recognition System

Facial recognition from an image or a video sequence draws attention for many image processing researchers owing to its myriad applications in real world as well as in computer vision, human-computer interaction and intelligent systems. Facial structures have unique features which can be extracted using some mathematical tools. We have used Principal Component Analysis (PCA) and Local Binary Pattern (LBP) to extract them and stored them in a database. When the query image is given the facial features are extracted and compared to the previously obtained results using Sparse Face recognition. Detailed test methods have been defined and an extensive testing of the algorithm has been performed on various standard databases. The results have been tabulated with required graphs. The proposed algorithm has been compared to other different algorithms which show significant improvement in results with small number of training samples. Finally the algorithm was integrated in a hardware system so that it can be used as a self sufficient portable system

New Methods for Discovering Hidden Dependence and for Assessing the Possible Influence of Unobserved Variables.

The biological interpretation of neuroimaging data often depends on changes in the dependence structure between locations in the brain. A major challenge in neuroscience is uncovering the relationship between consciousness and brain activity. Electroencephalography (EEG) recordings made on human subjects who are given anesthesia for surgery provide an opportunity to directly study this relationship. The main focus in this area has been on changes in the connectivity between brain regions that occur as the consciousness state changes. Connectivity can be assessed in terms of the statistical dependence between EEG measurements from different recording sites on the scalp. In this thesis, we consider two approaches for capturing changes in the dependence structure among several time series. We first consider the possibility that dependence between two series may be localized to a specific frequency band, and hence cannot be uncovered using global measures dependence. We propose methods to characterize the frequency-specific dependence in such data. We then consider the possibility that the dependence between two series can be revealed by applying a local transformation. We optimize over a class of such transformations to maximize a simple association measure, leading to a new measure of dependence for serially observed data. These two new methods are used to analyze a data that consists of multi-channel EEG recordings of multiple subjects under several consciousness states. Another question that arises in analyzing complex biological data sets is whether there exists an unobserved variable responsible for all apparent relationships between a given set of observed variables and the outcome. In the last chapter, we propose an approach to understanding under what circumstances a single unmeasured variable could explain the entire observed relationship between an outcome and several observed predictors. The unobservable regression of interest is characterized in terms of three quantities: the distribution of the unobserved covariate, the effect size of the unobserved covariate, and the net dependence between the unobserved and the observed covariates. We derive an explicit functional relationship among these quantities, and how this in turn can be used to learn about possible alternative explanations for an observed multiple regression relationship.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99959/1/ypa_1.pd