FACE IMAGE DE-OCCLUSION WITH VARIABLE-THRESHOLD ROBUST PCA

Master'sMASTER OF SCIENC

A comprehensive survey on Pose-Invariant Face Recognition

© 2016 ACM. The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed

A New Concept Of Dental Arch of Children in Normal Occlusion

The development of human dentition from adolescence to adulthood has been the subject of extensive study by numerous dentists, orthodontists and other experts in the past. While prevention and cure of dental diseases, surgical reconstitution to address teeth anomalies and research studies on teeth and development of the dental arch during the growing up years has been the main concerns across the past decades, in recent years, substantial effort has been evident in the field of mathematical analysis of the dental arch curve, particularly of children from varied age groups and diverse ethnic and national origins. The proper care and development of the primary dentition into permanent dentition is of major importance and the dental arch curvature, whose study has been related intimately by a growing number of dentists and orthodontists to the prospective achievement of ideal occlusion and normal permanent dentition, has eluded a proper definition of form and shape. Many eminent authors have put forth mathematical models to describe the teeth arch curve in humans. Some have imagined it as a parabola, ellipse or conic while others have viewed the same as a cubic spline. Still others have viewed the beta function as best describing the actual shape of the dental arch curve. Both finite mathematical functions as also polynomials ranging from 2nd order to 6th order have been cited as appropriate definitions of the arch in various studies by eminent authors. Each such model had advantages and disadvantages, but none could exactly define the shape of the human dental arch curvature and factor in its features like shape, spacing and symmetry/asymmetry. Recent advances in imaging techniques and computer-aided simulation have added to the attempts to determine dental arch form in children in normal occlusion. This paper presents key mathematical models & compares them through some secondary research study

Development of robust high performance face detector under different environmental conditions.

足利工業大学201

Investigating Social Interactions Using Multi-Modal Nonverbal Features

Every day, humans are involved in social situations and interplays, with the goal of sharing emotions and thoughts, establishing relationships with or acting on other human beings. These interactions are possible thanks to what is called social intelligence, which is the ability to express and recognize social signals produced during the interactions. These signals aid the information exchange and are expressed through verbal and non-verbal behavioral cues, such as facial expressions, gestures, body pose or prosody. Recently, many works have demonstrated that social signals can be captured and analyzed by automatic systems, giving birth to a relatively new research area called social signal processing, which aims at replicating human social intelligence with machines. In this thesis, we explore the use of behavioral cues and computational methods for modeling and understanding social interactions. Concretely, we focus on several behavioral cues in three specic contexts: rst, we analyze the relationship between gaze and leadership in small group interactions. Second, we expand our analysis to face and head gestures in the context of deception detection in dyadic interactions. Finally, we analyze the whole body for group detection in mingling scenarios

Towards a complete 3D morphable model of the human head

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of the human head to date that includes face, cranium, ears, eyes, teeth and tongue. To achieve this, we propose two methods for combining existing 3DMMs of different overlapping head parts: i. use a regressor to complete missing parts of one model using the other, ii. use the Gaussian Process framework to blend covariance matrices from multiple models. Thus we build a new combined face-and-head shape model that blends the variability and facial detail of an existing face model (the LSFM) with the full head modelling capability of an existing head model (the LYHM). Then we construct and fuse a highly-detailed ear model to extend the variation of the ear shape. Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity. The new model achieves state-of-the-art performance. We use our model to reconstruct full head representations from single, unconstrained images allowing us to parameterize craniofacial shape and texture, along with the ear shape, eye gaze and eye color.Comment: 18 pages, 18 figures, submitted to Transactions on Pattern Analysis and Machine Intelligence (TPAMI) on the 9th of October as an extension paper of the original oral CVPR paper : arXiv:1903.0378

A 3D Morphable Model of Craniofacial Shape and Texture Variation

We present a fully automatic pipeline to train 3D Mor- phable Models (3DMMs), with contributions in pose nor- malisation, dense correspondence using both shape and texture information, and high quality, high resolution tex- ture mapping. We propose a dense correspondence system, combining a hierarchical parts-based template morphing framework in the shape channel and a refining optical flow in the texture channel. The texture map is generated us- ing raw texture images from five views. We employ a pixel- embedding method to maintain the texture map at the same high resolution as the raw texture images, rather than us- ing per-vertex color maps. The high quality texture map is then used for statistical texture modelling. The Headspace dataset used for training includes demographic information about each subject, allowing for the construction of both global 3DMMs and models tailored for specific gender and age groups. We build both global craniofacial 3DMMs and demographic sub-population 3DMMs from more than 1200 distinct identities. To our knowledge, we present the first public 3DMM of the full human head in both shape and texture: the Liverpool-York Head Model. Furthermore, we analyse the 3DMMs in terms of a range of performance metrics. Our evaluations reveal that the training pipeline constructs state-of-the-art models

Facial Texture Super-Resolution by Fitting 3D Face Models

This book proposes to solve the low-resolution (LR) facial analysis problem with 3D face super-resolution (FSR). A complete processing chain is presented towards effective 3D FSR in real world. To deal with the extreme challenges of incorporating 3D modeling under the ill-posed LR condition, a novel workflow coupling automatic localization of 2D facial feature points and 3D shape reconstruction is developed, leading to a robust pipeline for pose-invariant hallucination of the 3D facial texture