3D Gaze Estimation from Remote RGB-D Sensors

The development of systems able to retrieve and characterise the state of humans is important for many applications and fields of study. In particular, as a display of attention and interest, gaze is a fundamental cue in understanding people activities, behaviors, intentions, state of mind and personality. Moreover, gaze plays a major role in the communication process, like for showing attention to the speaker, indicating who is addressed or averting gaze to keep the floor. Therefore, many applications within the fields of human-human, human-robot and human-computer interaction could benefit from gaze sensing. However, despite significant advances during more than three decades of research, current gaze estimation technologies can not address the conditions often required within these fields, such as remote sensing, unconstrained user movements and minimum user calibration. Furthermore, to reduce cost, it is preferable to rely on consumer sensors, but this usually leads to low resolution and low contrast images that current techniques can hardly cope with. In this thesis we investigate the problem of automatic gaze estimation under head pose variations, low resolution sensing and different levels of user calibration, including the uncalibrated case. We propose to build a non-intrusive gaze estimation system based on remote consumer RGB-D sensors. In this context, we propose algorithmic solutions which overcome many of the limitations of previous systems. We thus address the main aspects of this problem: 3D head pose tracking, 3D gaze estimation, and gaze based application modeling. First, we develop an accurate model-based 3D head pose tracking system which adapts to the participant without requiring explicit actions. Second, to achieve a head pose invariant gaze estimation, we propose a method to correct the eye image appearance variations due to head pose. We then investigate on two different methodologies to infer the 3D gaze direction. The first one builds upon machine learning regression techniques. In this context, we propose strategies to improve their generalization, in particular, to handle different people. The second methodology is a new paradigm we propose and call geometric generative gaze estimation. This novel approach combines the benefits of geometric eye modeling (normally restricted to high resolution images due to the difficulty of feature extraction) with a stochastic segmentation process (adapted to low-resolution) within a Bayesian model allowing the decoupling of user specific geometry and session specific appearance parameters, along with the introduction of priors, which are appropriate for adaptation relying on small amounts of data. The aforementioned gaze estimation methods are validated through extensive experiments in a comprehensive database which we collected and made publicly available. Finally, we study the problem of automatic gaze coding in natural dyadic and group human interactions. The system builds upon the thesis contributions to handle unconstrained head movements and the lack of user calibration. It further exploits the 3D tracking of participants and their gaze to conduct a 3D geometric analysis within a multi-camera setup. Experiments on real and natural interactions demonstrate the system is highly accuracy. Overall, the methods developed in this dissertation are suitable for many applications, involving large diversity in terms of setup configuration, user calibration and mobility

Video Interpolation using Optical Flow and Laplacian Smoothness

Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the local smoothness. Similar to Li et al., our approach adopts a mesh to the image with a resolution up to one vertex per pixel and uses angle constraints to ensure sensible local deformations between image pairs. The Laplacian Mesh constraints are expressed wholly inside the optical flow optimization, and can be applied in a straightforward manner to a wide range of image tracking and registration problems. We evaluate our approach by testing on several benchmark datasets, including the Middlebury and Garg et al. datasets. In addition, we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Face recognition in the wild.

Research in face recognition deals with problems related to Age, Pose, Illumination and Expression (A-PIE), and seeks approaches that are invariant to these factors. Video images add a temporal aspect to the image acquisition process. Another degree of complexity, above and beyond A-PIE recognition, occurs when multiple pieces of information are known about people, which may be distorted, partially occluded, or disguised, and when the imaging conditions are totally unorthodox! A-PIE recognition in these circumstances becomes really “wild” and therefore, Face Recognition in the Wild has emerged as a field of research in the past few years. Its main purpose is to challenge constrained approaches of automatic face recognition, emulating some of the virtues of the Human Visual System (HVS) which is very tolerant to age, occlusion and distortions in the imaging process. HVS also integrates information about individuals and adds contexts together to recognize people within an activity or behavior. Machine vision has a very long road to emulate HVS, but face recognition in the wild, using the computer, is a road to perform face recognition in that path. In this thesis, Face Recognition in the Wild is defined as unconstrained face recognition under A-PIE+; the (+) connotes any alterations to the design scenario of the face recognition system. This thesis evaluates the Biometric Optical Surveillance System (BOSS) developed at the CVIP Lab, using low resolution imaging sensors. Specifically, the thesis tests the BOSS using cell phone cameras, and examines the potential of facial biometrics on smart portable devices like iPhone, iPads, and Tablets. For quantitative evaluation, the thesis focused on a specific testing scenario of BOSS software using iPhone 4 cell phones and a laptop. Testing was carried out indoor, at the CVIP Lab, using 21 subjects at distances of 5, 10 and 15 feet, with three poses, two expressions and two illumination levels. The three steps (detection, representation and matching) of the BOSS system were tested in this imaging scenario. False positives in facial detection increased with distances and with pose angles above ± 15°. The overall identification rate (face detection at confidence levels above 80%) also degraded with distances, pose, and expressions. The indoor lighting added challenges also, by inducing shadows which affected the image quality and the overall performance of the system. While this limited number of subjects and somewhat constrained imaging environment does not fully support a “wild” imaging scenario, it did provide a deep insight on the issues with automatic face recognition. The recognition rate curves demonstrate the limits of low-resolution cameras for face recognition at a distance (FRAD), yet it also provides a plausible defense for possible A-PIE face recognition on portable devices

3D Reconstruction of 'In-the-Wild' Faces in Images and Videos

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record 3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and are among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions ('in-the-wild'). In this paper, we propose the first 'in-the-wild' 3DMM by combining a statistical model of facial identity and expression shape with an 'in-the-wild' texture model. We show that such an approach allows for the development of a greatly simplified fitting procedure for images and videos, as there is no need to optimise with regards to the illumination parameters. We have collected three new benchmarks that combine 'in-the-wild' images and video with ground truth 3D facial geometry, the first of their kind, and report extensive quantitative evaluations using them that demonstrate our method is state-of-the-art.Engineering and Physical Sciences Research Council (EPSRC