Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

We present techniques for improving performance driven facial animation, emotion recognition, and facial key-point or landmark prediction using learned identity invariant representations. Established approaches to these problems can work well if sufficient examples and labels for a particular identity are available and factors of variation are highly controlled. However, labeled examples of facial expressions, emotions and key-points for new individuals are difficult and costly to obtain. In this paper we improve the ability of techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression. We show how probabilistic modeling of these sources of variation allows one to learn identity-invariant representations for expressions which can then be used to identity-normalize various procedures for facial expression analysis and animation control. We also show how to extend the widely used techniques of active appearance models and constrained local models through replacing the underlying point distribution models which are typically constructed using principal component analysis with identity-expression factorized representations. We present a wide variety of experiments in which we consistently improve performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS

FAME - A Flexible Appearance Modelling Environment

Combined modelling of pixel intensities and shape has proven to be a very robust and widely applicable approach to interpret images. As such the Active Appearance Model (AAM) framework has been applied to a wide variety of problems within medical image analysis. This paper summarises AAM applications within medicine and describes a public domain implementation, namely the Flexible Appearance Modelling Environment (FAME). We give guidelines for the use of this research platform, and show that the optimisation techniques used renders it applicable to interactive medical applications. To increase performance and make models generalise better, we apply parallel analysis to obtain automatic and objective model truncation. Further, two different AAM training methods are compared along with a reference case study carried out on cross-sectional short-axis cardiac magnetic resonance images and face images. Source code and annotated data sets needed to reproduce the results are put in the public domain for further investigation

Weak biases emerging from vocal tract anatomy shape the repeated transmission of vowels

Linguistic diversity is affected by multiple factors, but it is usually assumed that variation in the anatomy of our speech organs plays no explanatory role. Here we use realistic computer models of the human speech organs to test whether inter-individual and inter-group variation in the shape of the hard palate (the bony roof of the mouth) affects acoustics of speech sounds. Based on 107 midsagittal MRI scans of the hard palate of human participants, we modelled with high accuracy the articulation of a set of five cross-linguistically representative vowels by agents learning to produce speech sounds. We found that different hard palate shapes result in subtle differences in the acoustics and articulatory strategies of the produced vowels, and that these individual-level speech idiosyncrasies are amplified by the repeated transmission of language across generations. Therefore, we suggest that, besides culture and environment, quantitative biological variation can be amplified, also influencing language

Preservation of Patient Level Privacy: Federated Classification and Calibration Models

With the launching of the Precision Medicine Initiative in the United States, by the National Institute of Health, and the emergence of a large volume of electronic health records, there are many opportunities to improve clinical decision support systems. A large number of samples are needed to build predictive models that have adequate discrimination and calibration. However, protecting patient privacy is also an important issue. Patient data are typically protected in localized silos, and consolidation of datasets from different healthcare systems is difficult. Federated learning allows the training of a global model by amassing intermediate calculations from localized medical systems. The knowledge learned from the data can be transferred and aggregated to achieve better performance than the one achieved by individual local models. Federated learning may help build better models, providing more accurate predictions. There are two types of measures to assess how well a model performs: discrimination and calibration. While most papers report discrimination measures, calibration has often been neglected but it is a critical metric for evaluation. In this dissertation, I show a novel way to build classifiers and calibration models in a federated manner. I also show how I can evaluate and improve model calibration in this manner. Federated modeling enables the accumulation of knowledge and information that are otherwise locked behind local medical systems