Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method. Six different teams from universities around the world have participated in the contest. Use of one or multiple techniques from motion, texture analysis and liveness detection appears to be the common trend in this competition. Most of the algorithms are able to clearly separate spoof attempts from real accesses. The results suggest the investigation of more complex attacks.

We faced the problem of detecting 2-D face spoofing attacks performed by placing a printed photo of a real user in\ud front of the camera. For this type of attack it is not possible to relay just on the face movements as a clue of vitality\ud because the attacker can easily simulate such a case, and\ud also because real users often show a “low vitality” during\ud the authentication session. In this paper, we perform both\ud video and static analysis in order to employ complementary\ud information about motion, texture and liveness and consequently to obtain a more robust classificatio

Nowadays, a very large number of digital image archives is easily produced thanks to the wide diffusion of personal digital cameras and mobile devices with embedded cameras. Thus, each personal computer, personal storage unit, as well as photo-sharing and social-network web sites, are rapidly becoming the repository for thousands, or even billions of images (i.e., more than 100 million photos are uploaded every day on the social site Facebook 1 ). As a consequence, there is an increasing need for tools enabling the semantic search, classification, and retrieval of images. The use of meta-data associated to images solves the problems only partially, as the process of assigning reliable meta data to images is not trivial, is slow, and closely related to whom performed the task. One solution for effective image search and retrieval is to combine content-based analysis with feedbacks from the users. In this paper we present Image Hunter, a tool that implements a Content Based Image Retrieval (CBIR) engine with a Relevance Feedback mechanism. Thanks to a user friendly interface the tool is especially suited to unskilled users. In addition, the modular structure permits the use of the same core both in web-based and stand alone applications.