In this paper we propose to cast the problem of identification of people, objects or places into an application for smart glasses that decodes information from graphical markers. We focus on analyzing different factors that can have influence on the processes of the automatic recognition of information from a code. The research we present aims at reviewing recognition performances in function of: size of a marker, distance from/to a marker, position of a marker and hardware used to decode information. Additionally, we describe a colorful graphical marker which was created for comparative analysis with existing monochrome codes. We also analyze accuracy of the detection of markers colors under different illumination conditions and different scanning distances. Moreover, considering possible usages of graphical markers, we present a prototype application, which can potentially provide faster access to patients' information. We believe that it may be very useful, because health care have to deal with a lot of various assets that could be identify or classify by using eyewear devices. Our first conclusions are the facts that following factors may have influence on monochrome code recognition (i) distance from the camera, (ii) rotation and slope of code, (iii) quality and size of the code. Distance from the camera should be adapted to the device that is used to scan the code. Under preliminary studies with colorful codes, tested light conditions and distances did not affect the classification of colors.