Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.

Ultracompact Retrodirective Antenna Arrays With Superdirective Radiation Patterns

It is shown that the direction-of-arrival (DoA) information carried by an incident electromagnetic (EM) wave can be encoded into the evanescent near field of an electrically small resonance antenna array with a spatial rate higher than that of the incident field oscillation rate in free space. Phase conjugation of the received signal leads to the retrodirection of the near field in the antenna array environment, which in turn generates a retrodirected far-field beam toward the original DoA. This EM phenomenon enables electrically small retrodirective antenna arrays with superdirective, angular super-resolution, auto-pointing properties for an arbitrary DoA. A theoretical explanation of the phenomenon based on first principal observations is given and full-wave simulations demonstrate a realizability route for the proposed retrodirective terminal that is comprised of resonance dipole antenna elements. Specifically, it is shown that a three-element disk-loaded retrodirective dipole array with 0.15\lambda spacings can achieve a 3.4-dBi maximal gain, 3-dBi front-to-back ratio, and 13% return loss fractional bandwidth (at the 10-dB level). Then, it is demonstrated that the radiation gain of a three-element array can be improved to approximately 6 dBi at the expense of the return loss fractional bandwidth reduction (2%).

Actin dynamics provides membrane tension to merge fusing vesicles into the plasma membrane

Vesicle fusion is executed via formation of an Ω-shaped structure (Ω-profile), followed by closure (kiss-and-run) or merging of the Ω-profile into the plasma membrane (full fusion). Although Ω-profile closure limits release but recycles vesicles economically, Ω-profile merging facilitates release but couples to classical endocytosis for recycling. Despite its crucial role in determining exocytosis/endocytosis modes, how Ω-profile merging is mediated is poorly understood in endocrine cells and neurons containing small ∼30-300 nm vesicles. Here, using confocal and super-resolution STED imaging, force measurements, pharmacology and gene knockout, we show that dynamic assembly of filamentous actin, involving ATP hydrolysis, N-WASP and formin, mediates Ω-profile merging by providing sufficient plasma membrane tension to shrink the Ω-profile in neuroendocrine chromaffin cells containing ∼300 nm vesicles. Actin-directed compounds also induce Ω-profile accumulation at lamprey synaptic active zones, suggesting that actin may mediate Ω-profile merging at synapses. These results uncover molecular and biophysical mechanisms underlying Ω-profile merging.

Human behaviour recognition in data-scarce domains

This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.