Fast, Illumination Insensitive Face Detection Based on Multilinear Techniques and Curvature Features

Abstrac

Geometric modeling of non-rigid 3D shapes : theory and application to object recognition.

One of the major goals of computer vision is the development of flexible and efficient methods for shape representation. This is true, especially for non-rigid 3D shapes where a great variety of shapes are produced as a result of deformations of a non-rigid object. Modeling these non-rigid shapes is a very challenging problem. Being able to analyze the properties of such shapes and describe their behavior is the key issue in research. Also, considering photometric features can play an important role in many shape analysis applications, such as shape matching and correspondence because it contains rich information about the visual appearance of real objects. This new information (contained in photometric features) and its important applications add another, new dimension to the problem\u27s difficulty. Two main approaches have been adopted in the literature for shape modeling for the matching and retrieval problem, local and global approaches. Local matching is performed between sparse points or regions of the shape, while the global shape approaches similarity is measured among entire models. These methods have an underlying assumption that shapes are rigidly transformed. And Most descriptors proposed so far are confined to shape, that is, they analyze only geometric and/or topological properties of 3D models. A shape descriptor or model should be isometry invariant, scale invariant, be able to capture the fine details of the shape, computationally efficient, and have many other good properties. A shape descriptor or model is needed. This shape descriptor should be: able to deal with the non-rigid shape deformation, able to handle the scale variation problem with less sensitivity to noise, able to match shapes related to the same class even if these shapes have missing parts, and able to encode both the photometric, and geometric information in one descriptor. This dissertation will address the problem of 3D non-rigid shape representation and textured 3D non-rigid shapes based on local features. Two approaches will be proposed for non-rigid shape matching and retrieval based on Heat Kernel (HK), and Scale-Invariant Heat Kernel (SI-HK) and one approach for modeling textured 3D non-rigid shapes based on scale-invariant Weighted Heat Kernel Signature (WHKS). For the first approach, the Laplace-Beltrami eigenfunctions is used to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the Collaborative Representation-based Classification with a Regularized Least Square (CRC-RLS) algorithm. The experimental results have shown that the proposed descriptor can achieve state-of-the-art results on two benchmark data sets. For the second approach, an improved method to introduce scale-invariance has been also proposed to avoid noise-sensitive operations in the original transformation method. Then a new 3D shape descriptor is formed based on the histograms of the scale-invariant HK for a number of critical points on the shape at different time scales. A Collaborative Classification (CC) scheme is then employed for object classification. The experimental results have shown that the proposed descriptor can achieve high performance on the two benchmark data sets. An important observation from the experiments is that the proposed approach is more able to handle data under several distortion scenarios (noise, shot-noise, scale, and under missing parts) than the well-known approaches. For modeling textured 3D non-rigid shapes, this dissertation introduces, for the first time, a mathematical framework for the diffusion geometry on textured shapes. This dissertation presents an approach for shape matching and retrieval based on a weighted heat kernel signature. It shows how to include photometric information as a weight over the shape manifold, and it also propose a novel formulation for heat diffusion over weighted manifolds. Then this dissertation presents a new discretization method for the weighted heat kernel induced by the linear FEM weights. Finally, the weighted heat kernel signature is used as a shape descriptor. The proposed descriptor encodes both the photometric, and geometric information based on the solution of one equation. Finally, this dissertation proposes an approach for 3D face recognition based on the front contours of heat propagation over the face surface. The front contours are extracted automatically as heat is propagating starting from a detected set of landmarks. The propagation contours are used to successfully discriminate the various faces. The proposed approach is evaluated on the largest publicly available database of 3D facial images and successfully compared to the state-of-the-art approaches in the literature. This work can be extended to the problem of dense correspondence between non-rigid shapes. The proposed approaches with the properties of the Laplace-Beltrami eigenfunction can be utilized for 3D mesh segmentation. Another possible application of the proposed approach is the view point selection for 3D objects by selecting the most informative views that collectively provide the most descriptive presentation of the surface

Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery

A robust and fast automatic moving object detection and tracking system is essential to characterize target object and extract spatial and temporal information for different functionalities including video surveillance systems, urban traffic monitoring and navigation, robotic. In this dissertation, I present a collaborative Spatial Pyramid Context-aware moving object detection and Tracking system. The proposed visual tracker is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called dynamically to assist master tracker. SPCT utilizes image spatial context at different level to make the video tracking system resistant to occlusion, background noise and improve target localization accuracy and robustness. We chose a pre-selected seven-channel complementary features including RGB color, intensity and spatial pyramid of HoG to encode object color, shape and spatial layout information. We exploit integral histogram as building block to meet the demands of real-time performance. A novel fast algorithm is presented to accurately evaluate spatially weighted local histograms in constant time complexity using an extension of the integral histogram method. Different techniques are explored to efficiently compute integral histogram on GPU architecture and applied for fast spatio-temporal median computations and 3D face reconstruction texturing. We proposed a multi-component framework based on semantic fusion of motion information with projected building footprint map to significantly reduce the false alarm rate in urban scenes with many tall structures. The experiments on extensive VOTC2016 benchmark dataset and aerial video confirm that combining complementary tracking cues in an intelligent fusion framework enables persistent tracking for Full Motion Video and Wide Aerial Motion Imagery.Comment: PhD Dissertation (162 pages

Face modeling for face recognition in the wild.

Face understanding is considered one of the most important topics in computer vision field since the face is a rich source of information in social interaction. Not only does the face provide information about the identity of people, but also of their membership in broad demographic categories (including sex, race, and age), and about their current emotional state. Facial landmarks extraction is the corner stone in the success of different facial analyses and understanding applications. In this dissertation, a novel facial modeling is designed for facial landmarks detection in unconstrained real life environment from different image modalities including infra-red and visible images. In the proposed facial landmarks detector, a part based model is incorporated with holistic face information. In the part based model, the face is modeled by the appearance of different face part(e.g., right eye, left eye, left eyebrow, nose, mouth) and their geometric relation. The appearance is described by a novel feature referred to as pixel difference feature. This representation is three times faster than the state-of-art in feature representation. On the other hand, to model the geometric relation between the face parts, the complex Bingham distribution is adapted from the statistical community into computer vision for modeling the geometric relationship between the facial elements. The global information is incorporated with the local part model using a regression model. The model results outperform the state-of-art in detecting facial landmarks. The proposed facial landmark detector is tested in two computer vision problems: boosting the performance of face detectors by rejecting pseudo faces and camera steering in multi-camera network. To highlight the applicability of the proposed model for different image modalities, it has been studied in two face understanding applications which are face recognition from visible images and physiological measurements for autistic individuals from thermal images. Recognizing identities from faces under different poses, expressions and lighting conditions from a complex background is an still unsolved problem even with accurate detection of landmark. Therefore, a learning similarity measure is proposed. The proposed measure responds only to the difference in identities and filter illuminations and pose variations. similarity measure makes use of statistical inference in the image plane. Additionally, the pose challenge is tackled by two new approaches: assigning different weights for different face part based on their visibility in image plane at different pose angles and synthesizing virtual facial images for each subject at different poses from single frontal image. The proposed framework is demonstrated to be competitive with top performing state-of-art methods which is evaluated on standard benchmarks in face recognition in the wild. The other framework for the face understanding application, which is a physiological measures for autistic individual from infra-red images. In this framework, accurate detecting and tracking Superficial Temporal Arteria (STA) while the subject is moving, playing, and interacting in social communication is a must. It is very challenging to track and detect STA since the appearance of the STA region changes over time and it is not discriminative enough from other areas in face region. A novel concept in detection, called supporter collaboration, is introduced. In support collaboration, the STA is detected and tracked with the help of face landmarks and geometric constraint. This research advanced the field of the emotion recognition

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Differential Tracking through Sampling and Linearizing the Local Appearance Manifold

Recovering motion information from input camera image sequences is a classic problem of computer vision. Conventional approaches estimate motion from either dense optical flow or sparse feature correspondences identified across successive image frames. Among other things, performance depends on the accuracy of the feature detection, which can be problematic in scenes that exhibit view-dependent geometric or photometric behaviors such as occlusion, semitransparancy, specularity and curved reflections. Beyond feature measurements, researchers have also developed approaches that directly utilize appearance (intensity) measurements. Such appearance-based approaches eliminate the need for feature extraction and avoid the difficulty of identifying correspondences. However the simplicity of on-line processing of image features is usually traded for complexity in off-line modeling of the appearance function. Because the appearance function is typically very nonlinear, learning it usually requires an impractically large number of training samples. I will present a novel appearance-based framework that can be used to estimate rigid motion in a manner that is computationally simple and does not require global modeling of the appearance function. The basic idea is as follows. An n-pixel image can be considered as a point in an n-dimensional appearance space. When an object in the scene or the camera moves, the image point moves along a low-dimensional appearance manifold. While globally nonlinear, the appearance manifold can be locally linearized using a small number of nearby image samples. This linear approximation of the local appearance manifold defines a mapping between the images and the underlying motion parameters, allowing the motion estimation to be formulated as solving a linear system. I will address three key issues related to motion estimation: how to acquire local appearance samples, how to derive a local linear approximation given appearance samples, and whether the linear approximation is sufficiently close to the real local appearance manifold. In addition I will present a novel approach to motion segmentation that utilizes the same appearance-based framework to classify individual image pixels into groups associated with different underlying rigid motions