Grm, Klemen; Štruc, Vitomir
Deep face recognition for surveillance applications Journal Article
In: IEEE Intelligent Systems, vol. 33, no. 3, pp. 46–50, 2018.
Automated person recognition from surveillance quality footage is an open research problem with many potential application areas. In this paper, we aim at addressing this problem by presenting a face recognition approach tailored towards surveillance applications. The presented approach is based on domain-adapted convolutional neural networks and ranked second in the International Challenge on Biometric Recognition in the Wild (ICB-RW) 2016. We evaluate the performance of the presented approach on part of the Quis-Campi dataset and compare it against several existing face recognition techniques and one (state-of-the-art) commercial system. We find that the domain-adapted convolutional network outperforms all other assessed techniques, but is still inferior to human performance.
Kenk, Vildana Sulič; Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon
Smart surveillance technologies in border control Journal Article
In: European Journal of Law and Technology, vol. 4, no. 2, 2013.
The paper addresses the technical and legal aspects of the existing and forthcoming intelligent ('smart') surveillance technologies that are (or are considered to be) employed in the border control application area. Such technologies provide a computerized decision-making support to border control authorities, and are intended to increase the reliability and efficiency of border control measures. However, the question that arises is how effective these technologies are, as well as at what price, economically, socially, and in terms of citizens' rights. The paper provides a brief overview of smart surveillance technologies in border control applications, especially those used for controlling cross-border traffic, discusses possible proportionality issues and privacy risks raised by the increasingly widespread use of such technologies, as well as good/best practises developed in this area. In a broader context, the paper presents the result of the research carried out as part of the SMART (Scalable Measures for Automated Recognition Technologies) project.