The ear as a biometric

It is more than 10 years since the first tentative experiments in ear biometrics were conducted and it has now reached the “adolescence” of its development towards a mature biometric. Here we present a timely retrospective of the ensuing research since those early days. Whilst its detailed structure may not be as complex as the iris, we show that the ear has unique security advantages over other biometrics. It is most unusual, even unique, in that it supports not only visual and forensic recognition, but also acoustic recognition at the same time. This, together with its deep three-dimensional structure and its robust resistance to change with age will make it very difficult to counterfeit thus ensuring that the ear will occupy a special place in situations requiring a high degree of protection

Multi-scale Crowd Feature Detection using Vision Sensing and Statistical Mechanics Principles

Crowd behaviour analysis using vision has been subject to many different approaches. Multi-purpose crowd descriptors are one of the more recent approaches. These descriptors provide an opportunity to compare and categorise various types of crowds as well as classify their respective behaviours. Nevertheless, the automated calculation of descriptors which are expressed as measurements with accurate interpretation is a challenging problem. In this paper, analogies between human crowds and molecular thermodynamics systems are drawn for the measurement of crowd behaviour. Specifically, a novel descriptor is defined and measured for crowd behaviour at multiple scales. This descriptor uses the concept of Entropy for evaluating the state of crowd disorder. By results, the descriptor Entropy does indeed appear to capture the desired outcome for crowd entropy while utilizing easily detectable image features. Our new approach for machine understanding of crowd behaviour is promising, while it offers new complementary capabilities to the existing crowd descriptors, for example, as will be demonstrated, in the case of spectator crowds. The scope and performance of this descriptor is further discussed in details in this paper

Geospatial intelligence and visual classification of environmentally observed species in the future Internet

The rapid development of advanced smart communication tools with good quality and resolution video cameras,audio and GPS devices in the last few years shall lead to profound impacts on the way future environmentalobservations are conducted and accessed by communities. The resulting large scale interconnections of these"Future Internet Things" form a large environmental sensing network which will generate large volumes of qualityenvironmental observations and at highly localised spatial scales. This enablement in environmental sensing atlocal scales will be of great importance to contribute in the study of fauna and flora in the near future, particularlyon the effect of climate change on biodiversity in various regions of Europe and beyond. The Future Internet couldalso potentially become the de facto information space to provide participative real-time sensing by communitiesand improve our situation awarness of the effect of climate on local environments. In the ENVIROFI(2011-2013)Usage Area project in the FP7 FI-PPP programme, a set of requirements for specific (and generic) enablers isachieved with the potential establishement of participating community observatories of the future. In particular,the specific enablement of interest concerns the building of future interoperable services for the management ofenvironmental data intelligently with tagged contextual geo-spatial information generated by multiple operatorsin communities (Using smart phones). The classification of observed species in the resulting images is achievedwith structured data pre-processing, semantic enrichement using contextual geospatial information, and high levelfusion with controlled uncertainty estimations. The returned identification of species is further improved usingfuture ground truth corrections and learning by the specific enablers

Shaped Wavelets for Curvilinear Structures for Ear Biometrics

One of the most recent trends in biometrics is recognition by ear ap-pearance in head profile images. Determining the region of interest which con-tains the ear is an important step in an ear biometric system. To this end, we propose a robust, simple and effective method for ear detection from profile im-ages by employing a bank of curved and stretched Gabor wavelets, known as banana wavelets. A 100% detection rate is achieved here on a group of 252 pro-file images from XM2VTS database. The banana wavelets technique demon-strates better performances than Gabor wavelets technique. This indicates that the curved wavelets are advantageous here. Also the banana wavelet technique is applied to a new and more challenging database which highlights practical considerations of a more realistic deployment. This ear detection technique is fully automated, has encouraging performance and appears to be robust to de-gradation by noise

EO Big Data connectors and analytics for understanding the effects of climate change on migratory trends of marine wildlife

This paper describes the current ongoing research activities concerning the intelligent management and processing of Earth Observation (EO) big data together with the implementation of data connectors, advanced data analytics and Knowledge Base services to a Big Data platform in the EO4Wildlife project (www.eo4wildlife.eu). These components support on the discovery of marine wildlife migratory behaviours, some of which may be a direct consequence of the changing Met-Ocean resources and the globe climatic changes. In EO4wildlife, we specifically focus on the implementation of web-enabled advanced analytics web services which comply with OGC standards and make them accessible to a wide research community for investigating on trends of animal behaviour around specific marine regions of interest. Big data connectors and a catalogue service are being installed to enable access to COPERNICUS sentinels and ARGOS satellite big data together with other in situ heterogeneous sources. Furthermore, data mining services are being developed for knowledge extraction on species habitats and temporal behaviour trends. Also, high level fusion and reasoning services which process big data observations are deployed to forecast marine wild-life behaviour with estimated uncertainties. These will be tested and demonstrated under targeted thematic scenarios in EO4wildlife using a Big Data platform a cloud resources