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Responsible Research and Innovation (RRI) is an approach that anticipates and assesses potential implications and societal expectations with regard to research and innovation, with the aim to foster the design of inclusive and sustainable research and innovation. While RRI includes many aspects, in certain types of projects ethics and particularly privacy, is arguably the most sensitive topic. The objective in Horizon 2020 innovation project Eyes of Things (EoT) is to build a small high-performance, low-power, computer vision platform (similar to a smart camera) that can work independently and also embedded into all types of artefacts. In this paper, we describe the actions taken within the project related to ethics and privacy. A privacy-by-design approach has been followed, and work continues now in four platform demonstrators.

Current indoor navigation solutions face many problems in hospitals. Heavy electro-magnetic interference from hospital equipment affects the compass on a mobile device. Existing infrastructure may also need to be replaced or modified to provide better signal triangulation indoors. Current solutions are challenging in terms of cost (for both hospitals and users) and disruption of normal operation. In this context, this paper introduces the patent-pending Smart Indoor navigation and way finding system. With Smart Indoor, the user only has to point his smartphone to any sign inside the hospital. The recognized text is sent to a server which contains a database of sign texts along with their position inside the building. This paper describes the computer vision task of recognizing texts with a smartphone, arguably the most important module of the whole system. Preliminary results show that the proposed approach can be effective for such Smartphone-based indoor navigation system.

Automatic facial analysis abilities are commonly integrated in a system by a previous off-line learning stage. In this paper we argue that a facial analysis system would improve its facial analysis capabilities based on its own experience similarly to the way a biological system, i.e. the human system, does throughout the years. The approach described, focused on gender classification, updates its knowledge according to the classification results. The presented gender experiments suggest that this approach is promising, even when just a short simulation of what for humans would take years of acquisition experience was performed.

In this work local binary patterns based focus measures are presented. Local binary patterns (LBP) have been introduced in computer vision tasks like texture classification or face recognition. In applications where recognition is based on LBP, a computational saving can be achieved with the use of LBP in the focus measures. The behavior of the proposed measures is studied to test if they fulfill the properties of the focus measures and then a comparison with some well know focus measures is carried out in different scenarios.

The physical appearance and behavior of a robot is an important asset in terms of human-computer interaction. Multimodality is also fundamental, as we humans usually expect to interact in a natural way with voice, gestures, etc. people approach complex interaction devices with stances similar to those used in their interaction with other people. In this paper we describe a robot head, currently under development, that aims to be a multimodal (vision, voice, gestures, ...) perceptual user interface. Modules are described for face detection, tracking, facial movement, action selection and sound localization. Preliminary results indicate that the robot head can potentially achieve the goals we are interested in, namely human interaction and assistance.

The Eyes of Things (EoT) EU H2020 project envisages a computer vision platform that can be used both standalone and embedded into more complex artifacts, particularly for wearable applications, robotics, home products, surveillance etc. The core hardware will be based on a number of technologies and components that have been designed for maximum performance of the always-demanding vision applications while keeping the lowest energy consumption. An important functionality is to be able to communicate with other devices that we use everyday (say, configuring and controlling the EoT device from a tablet). Apart from low-power hardware components, an efficient protocol is necessary. Text-oriented protocols like HTTP are not appropriate in this context. Instead, the lightweight publish/subscribe MQTT protocol was selected. Still, the typical scenario is that of a device that sends/receives messages, the messages being forwarded by a cloud-based message broker. In this paper we propose a novel approach in which each EoT device acts as an MQTT broker instead of the typical cloud-based architecture. This eliminates the need for an external Internet server, which not only makes the whole deployment more affordable and simpler but also more secure by default.