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Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. The aim of this paper is to present a qualitative assessment of the morphing attack issue, and the challenges it entails, highlighting both the technological and human aspects of the problem. Here, after the face morphing attack scenario is presented, the paper provides an overview of the relevant bibliography and recent advances towards two central directions. First, the morphing of face images is outlined with a particular focus on the three main steps that are involved in the process, namely, landmark detection, face alignment and blending. Second, the detection of morphing attacks is presented under the prism of the so-called on-line and off-line detection scenarios and whether the proposed techniques employ handcrafted features, using classical methods, or automatically generated features, using deep-learning-based methods. The paper, then, presents the evaluation metrics that are employed in the corresponding bibliography and concludes with a discussion on open challenges that need to be address for further advancing automatic detection of morphing attacks. Despite the progress being made, the general consensus of the research community is that significant effort and resources are needed in the near future for the mitigation of the issue, especially, towards the creation of datasets capturing the full extent of the problem at hand and the availability of reference evaluation procedures for comparing novel automatic attack detection algorithms.

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Mobile phones and especially smartphones have been embraced by a rapidly increasing number of people worldwide and this trend is expected to evolve even more in the years to come. There are numerous smartphone Apps that record critical medical data in an effort to solve a particular health issue each time. We studied such applications and not surprisingly, we have found that development and design effort is often repeated. Software patterns have been detected to exist, however re-usability has not been enforced. This leads to lost programming manpower and to increased probability of repeating bugs in Apps. Moreover, at the moment smartphone e-Health Apps demand time, effort and costs for development. Unfortunately even simple data recording Apps are practically impossible to be produced by multiple health domain users who are not developers. In this work, we propose, design and implement a simple and integrated solution which gives healthcare professionals and researchers the ability to create their own data intensive smartphone applications, independent of the desired healthcare domain. The proposed approach applies efficient software techniques that hide development from the users and enable App creation through a simple Web User Interface. The Apps produced are in native format and it is possible to dynamically receive m-Health business logic and the chosen UI. Evaluation of the proposed solution has shown that the generated Apps are functionally and UI equivalent to human-coded Apps according to a number of comparison parameters. Furthermore, e-Health professionals show particular interest in developing Apps on their own for a particular domain they focus on.

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