Nonrigid face alignment for unknown subject in video

Non-rigid face alignment is a very important task in a large range of applications but the existing tracking based non-rigid face alignment methods are either inaccurate or requiring person-specific model. This dissertation has developed simultaneous alignment algorithms that overcome these constraints and provide alignment with high accuracy, efficiency, robustness to varying image condition, and requirement of only generic model.

Anchored deformable face ensemble alignment

At present, many approaches have been proposed for deformable face alignment with varying degrees of success. However, the common drawback to nearly all these approaches is the inaccurate landmark registrations. The registration errors which occur are predominantly heterogeneous (i.e. low error for some frames in a sequence and higher error for others). In this paper we propose an approach for simultaneously aligning an ensemble of deformable face images stemming from the same subject given noisy heterogeneous landmark estimates. We propose that these initial noisy landmark estimates can be used as an “anchor” in conjunction with known state-of-the-art objectives for unsupervised image ensemble alignment. Impressive alignment performance is obtained using well known deformable face fitting algorithms as “anchors.

Combining face and facial feature detectors for face detection performance improvement

[EN]In this paper, we experimentally study the combination of face and facial feature detectors to improve face detection performance. The face detection problem, as suggeted by recent face detection challenges, is still not solved. Face detectors traditionally fail in large-scale problems and/or when the face is occluded or di erent head rotations are present. The combination of face and facial feature detectors is evaluated with a public database. The obtained results evidence an improvement in the positive detection rate while reducing the false detection rate. Additionally, we prove that the integration of facial feature detectors provides useful information for pose estimation and face alignment.

Person-independent facial expression detection using constrained local models

In automatic facial expression detection, very accurate registration is desired which can be achieved via a deformable model approach where a dense mesh of 60-70 points on the face is used, such as an active appearance model (AAM). However, for applications where manually labeling frames is prohibitive, AAMs do not work well as they do not generalize well to unseen subjects. As such, a more coarse approach is taken for person-independent facial expression detection, where just a couple of key features (such as face and eyes) are tracked using a Viola-Jones type approach. The tracked image is normally post-processed to encode for shift and illumination invariance using a linear bank of filters. Recently, it was shown that this preprocessing step is of no benefit when close to ideal registration has been obtained. In this paper, we present a system based on the Constrained Local Model (CLM) which is a generic or person-independent face alignment algorithm which gains high accuracy. We show these results against the LBP feature extraction on the CK+ and GEMEP datasets.

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

Gradual training of cascaded shape regression for facial landmark localization and pose estimation

Facial landmarks play an important role in face recognition. They serve different steps of the recognition such as pose estimation, face alignment, and local feature extraction. Recently, cascaded shape regression has been proposed to accurately locate facial landmarks. A large number of weak regressors are cascaded in a sequence to fit face shapes to the correct landmark locations. In this paper, we propose to improve the method by applying gradual training. With this training, the regressors are not directly aimed to the true locations. The sequence instead is divided into successive parts each of which is aimed to intermediate targets between the initial and the true locations. We also investigate the incorporation of pose information in the cascaded model. The aim is to find out whether the model can be directly used to estimate head pose. Experiments on the Annotated Facial Landmarks in the Wild database have shown that the proposed method is able to improve the localization and give accurate estimates of pose.

Rank minimization across appearance and shape for AAM ensemble fitting

Active Appearance Models (AAMs) employ a paradigm of inverting a synthesis model of how an object can vary in terms of shape and appearance. As a result, the ability of AAMs to register an unseen object image is intrinsically linked to two factors. First, how well the synthesis model can reconstruct the object image. Second, the degrees of freedom in the model. Fewer degrees of freedom yield a higher likelihood of good fitting performance. In this paper we look at how these seemingly contrasting factors can complement one another for the problem of AAM fitting of an ensemble of images stemming from a constrained set (e.g. an ensemble of face images of the same person).

Deformable face ensemble alignment with robust grouped-L1 anchors

Many methods exist at the moment for deformable face fitting. A drawback to nearly all these approaches is that they are (i) noisy in terms of landmark positions, and (ii) the noise is biased across frames (i.e. the misalignment is toward common directions across all frames). In this paper we propose a grouped $\mathcal{L}1$-norm anchored method for simultaneously aligning an ensemble of deformable face images stemming from the same subject, given noisy heterogeneous landmark estimates. Impressive alignment performance improvement and refinement is obtained using very weak initialization as "anchors".

Visual front-end wars : Viola-Jones face detector vs Fourier Lucas-Kanade

The performance of visual speech recognition (VSR) systems are significantly influenced by the accuracy of the visual front-end. The current state-of-the-art VSR systems use off-the-shelf face detectors such as Viola- Jones (VJ) which has limited reliability for changes in illumination and head poses. For a VSR system to perform well under these conditions, an accurate visual front end is required. This is an important problem to be solved in many practical implementations of audio visual speech recognition systems, for example in automotive environments for an efficient human-vehicle computer interface. In this paper, we re-examine the current state-of-the-art VSR by comparing off-the-shelf face detectors with the recently developed Fourier Lucas-Kanade (FLK) image alignment technique. A variety of image alignment and visual speech recognition experiments are performed on a clean dataset as well as with a challenging automotive audio-visual speech dataset. Our results indicate that the FLK image alignment technique can significantly outperform off-the shelf face detectors, but requires frequent fine-tuning.

Achieving robust face recognition from video by combining a weak photometric model and a learnt generic face invariant

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. The central contribution is an illumination invariant, which we show to be suitable for recognition from video of loosely constrained head motion. In particular there are three contributions: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation to exploit the proposed invariant and generalize in the presence of extreme illumination changes; (ii) we introduce a video sequence re-illumination algorithm to achieve fine alignment of two video sequences; and (iii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve robustness to unseen head poses. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 323 individuals and 1474 video sequences with extreme illumination, pose and head motion variation. Our system consistently achieved a nearly perfect recognition rate (over 99.7% on all four databases). © 2012 Elsevier Ltd All rights reserved.

Palynostratigraphic alignment chronology versus independent dating methods. 14C, OSL and tephra: an example from Lake Fimon, northern Italy

New independent dating evidence is presented for a lacustrine record for which an age-depth model had already been derived through the interpretation of the pollen signal. Quartz OSL ages support radiocarbon ages that were previously considered to suffer an underestimation due to contamination, and imply a younger chronology for the core. The successful identification of the Campanian Ignimbrite as a cryptotephra within the core also validates this younger chronology, as well as extending the known geographical range of this tephra layer within Italy. These new results suggest that care should always be taken when building chronologies from proxy records that are correlated to the tuned records from which the global signal is often derived (i.e. double tuning). We do not offer this as the definitive chronology for Lake Fimon, but multiple lines of dating evidence show that there is sufficient reason to seriously consider it. The Quaternary dating community should always have all age information available, even when significant temporal offsets are apparent between various lines of evidence to be: 1) better informed when they face similar dilemmas in the future and 2) allow multiple working hypotheses to be considered.

Structure, rheology and shear alignment of Pluronic block copolymer mixtures

The structure and flow behaviour of binary mixtures of Pluronic block copolymers P85 and P123 is investigated by small-angle scattering, rheometry and mobility tests. Micelle dimensions are probed by dynamic light scattering. The micelle hydrodynamic radius for the 50/50 mixture is larger than that for either P85 or P123 alone, Clue to the formation of mixed micelles with a higher association number. The phase diagram for 50/50 mixtures contains regions Of Cubic and hexagonal phases similar to those for the parent homopolymers, however the region of stability of the cubic phase is enhanced at low temperature and concentrations above 40 wt%. This is ascribed to favourable packing of the mixed micelles containing core blocks with two different chain lengths, but similar corona chain lengths. The shear flow alignment of face-centred cubic and hexagonal phases is probed by in situ small-angle X-ray or neutron scattering with simultaneous rheology. The hexagonal phase can be aligned using steady shear in a Couette geometry, however the high modulus Cubic phase cannot be aligned well in this way. This requires the application of oscillatory shear or compression. (C) 2008 Elsevier Inc. All rights reserved.

One to one eMarketing strategy alignment: five internet case studies

Despite widespread adoption of new online business models and the rise of online or eMarketing using the Internet (Net) and its current graphical face the World Wide Web (Web), there remains a need for empirical research into specific aspects of eMarketing strategy beyond clickthrough analysis, Web audience analysis and scrutiny of Websites properties to mention the most widely examined elements. This paper discusses Peppers and Rogers (1999) OnetoOne strategy matrix and synthesises their approach with a series of personal interviews with business managers, drawn from a broader empirical study. It does so in an endeavour to improve outcomes from eMarketing strategy and the use of individual Net technologies. The aim is to aid both business practitioner and academician alike in further improving the effectiveness and efficiency gained from using converging technologies in eBusiness.

The alignment of market research with business strategy and CRM

This study investigated the alignment between market research, business strategy and customer relationship management (CRM). With the introduction of advanced IT systems which collect and model internal customer data (CRM), marketing managers face new choices when seeking information to facilitate their particular business strategy. A survey of market research (MR) and CRMusage was conducted among 207 marketing managers. The findings showed the Miles and Snow Prospector strategists placed greater reliance on both MR and internal CRM data systems than did Defenders. This applied in their tasks of developing new strategies and in enlisting senior management support for their actions. As Prospectors were the most reliant on both traditional market research and CRM systems, they need to be skilled in using CRM in conjunction with traditional research methods. Marketing managers who use CRM will need to foster functional relationships with the technical specialists who run CRM systems.