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

Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling

The increased deployment of intermittent renewable energy generators opens up opportunities for grid-connected energy storage. Batteries offer significant flexibility but are relatively expensive at present. Battery lifetime is a key factor in the business case, and it depends on usage, but most techno-economic analyses do not account for this. For the first time, this paper quantifies the annual benefits of grid-connected batteries including realistic physical dynamics and nonlinear electrochemical degradation. Three lithium-ion battery models of increasing realism are formulated, and the predicted degradation of each is compared with a large-scale experimental degradation data set (Mat4Bat). A respective improvement in RMS capacity prediction error from 11\% to 5\% is found by increasing the model accuracy. The three models are then used within an optimal control algorithm to perform price arbitrage over one year, including degradation. Results show that the revenue can be increased substantially while degradation can be reduced by using more realistic models. The estimated best case profit using a sophisticated model is a 175% improvement compared with the simplest model. This illustrates that using a simplistic battery model in a techno-economic assessment of grid-connected batteries might substantially underestimate the business case and lead to erroneous conclusions

COGNITIVE AND MOTOR PROCESSES IN A VOLLEYBALL SPECIFIC ANTICIPATION TASK

The purpose of the study was to investigate the relationship between cognitive and motor processes in a volleyball specific anticipation task with temporal video occlusion. Ten middle blockers of the national 2nd-4th division volleyball league were supposed to anticipate the setting direction presented on a screen, which occluded at three different occlusion times and respond with either a specific blocking movement (motor response) or key press (cognitive response). The participants showed only small significant differences in the kinematics of the block movement between the examined occlusion conditions. The participants showed a variable time of movement initiation with a shorter total duration with later occlusion although the movement time was shorter. The results illustrate a close temporal adaptation of the block movement to the presented setting situation

USING UNSUPERVISED LEARNING TO CHARACTERIZE MOVEMENT PATTERNS – AN EXPLORATIVE APPROACH

The purpose of this study was to explore the ability of Kohonen neural network self-organizing maps (SOM) to visualize and characterize different movement patterns during sidestepping. The marker trajectories of 631 sidestepping trials were used to train a SOM. Out of 63731 input vectors, the SOM identified 1250 unique stick figures, determined by the markers. Visualizing the movement trajectories and adding the latent parameter time, allows for the investigation of different movement patterns. Additionally, the SOM can be used to identify zones with increased injury risk, by adding more latent parameters which opens the option to monitor athletes and give feedback. The results highlight the ability of unsupervised learning to visualize movement patterns and to give further insight into an individual athlete’s status without the necessity of a-priory assumptions

Automatic Detection of Cone Photoreceptors In Split Detector Adaptive Optics Scanning Light Ophthalmoscope Images

Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice’s coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice’s coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images

Open Source Software for Automatic Detection of Cone Photoreceptors in Adaptive Optics Ophthalmoscopy Using Convolutional Neural Networks

Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online

Experiments in Diversifying Flickr Result Sets

The 2013 MediaEval Retrieving Diverse Social Images Task looked to tackling the problem of search result diversification of Flickr results sets formed from queries about geographic places and landmarks. In this paper we describe our approach of using a min-max similarity diversifier coupled with pre-filters and a reranker. We also demonstrate a number of novel features for measuring similarity to use in the diversification step

REDUCED MOVEMENT ADAPTABILITY IN SIDESTEPPING – A POSSIBLE SOURCE OF INJURY RISK

Adapting to different task constraints provides insight into how malleable an athlete’s movement dynamics are. The purpose of this pilot study was to investigate whether athletes can adequately change their preferred movement strategy during sidestepping when exposed to a manipulation task. Reduced movement adaptability was hypothesized to be one risk factor for ACL injuries. Fourteen male team sport athletes were investigated. The response to the manipulation task was intra-individual, with rearfoot strikers being less able to adapt their movement strategy and the resulting movement was even higher associated with ACL risk factors. Forefoot strikers were able to adapt their movement. This suggests, that athletes need to be investigated individually as group-based analyses might cover effects and that movement adaptability should be considered when evaluating injury risk