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Event-based cameras are novel vision sensors that respond to local variations in intensity, generating asynchronous pixels, referred to as events, with low latency, high temporal resolution, and high dynamic range. These events contain information related to the spatio-temporal dynamics of a scene. Given the temporal nature of the asynchronous event stream, several authors have contributed to recognising deformable objects in motion, specifically gestures. However, another category of deformable objects, such as facial expressions, has yet to be adequately addressed. In this paper, we present a comprehensive review of two topics of interest in novel event-based cameras: gesture and facial expression recognition. For both tasks, we evaluate two existing state-of-the-art learning models, and also we use a third model that learns from temporal and spatial correlations of events. To this end, we evaluate a wide range of classification models across multiple scenarios, analysing: the time/event cut-off window of the sample, the number of samples per class for each database, the spatial resolution of the databases, amongst other factors. In the case of gesture recognition, we utilise existing databases, while in the case of facial expression recognition we have synthetically generated two completely new databases (based on two state-of-the-art image databases): e-CK+ and e-MMI, with promising results for the future of this area. Finally, we provide our contributions to the community, specifically the databases developed and used for this study.

The introduction of uncontrollable renewable energy is having a positive impact on our health, the climate, and the economy, but it is also pushing the limits of the power system. The main reason for this is that, in any power system, the generation and consumption must match each other at all times. Thus, if we want to further introduce uncontrollable generation, we need a large ability to manage the demand. However, the ability to control the power consumption of existing demand management approaches is limited, and most of these approaches cannot contribute to the introduction of reneweables, because they do not consider distributed uncontrolled consumption and generation in the control. Furthermore, these methods do not allow users to exchange or jointly manage their power generation and consumption. In this context, we propose an augmented energy management model for prosumers (i.e., producer and consumer). This model considers controlled and uncontrolled generation and consumption, as well as the prosumer’s ability (i) to plan the intended power consumption; and (ii) to manage real-time deviations from the intended consumption. We apply this model to the energy management of prosumer communities, by allowing the prosumers to coordinate their power consumption plan, to manage the deviations from the intended consumption, and to help each other by compensating deviations. The proposed approach seeks to enhance the power system, and to enable a prosumer society that takes account social and environmental issues, as well as each prosumer’s quality of life.

The aim of this work is to carry out a comparative study of face recognition methods that are suitable to work in unconstrained environments. The analyzed methods are selected by considering their performance in former comparative studies, in addition to be real-time, to require just one image per person, and to be fully online. In the study two local-matching methods, histograms of LBP features and Gabor Jet descriptors, one holistic method, generalized PCA, and two image-matching methods, SIFT-based and ERCF-based, are analyzed. The methods are compared using the FERET, LFW, UCHFaceHRI, and FRGC databases, which allows evaluating them in real-world conditions that include variations in scale, pose, lighting, focus, resolution, facial expression, accessories, makeup, occlusions, background and photographic quality. Main conclusions of this study are: there is a large dependence of the methods on the amount of face and background information that is included in the face's images, and the performance of all methods decreases largely with outdoor-illumination. The analyzed methods are robust to inaccurate alignment, face occlusions, and variations in expressions, to a large degree. LBP-based methods are an excellent election if we need real-time operation as well as high recognition rates.