How far did we get in face spoofing detection?
This is an incremental survey that addresses the need for accurate face spoofing detection in access control systems, summarizing existing research to guide future work.
The paper presents a structured survey analyzing face spoofing detection works from the last decade, categorizing them by descriptors and classifiers, and providing a comparative analysis using key public datasets to observe trends and discuss open issues.
The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings the temporal evolution of the face spoofing detection field, as well as a comparative analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe trends in the existing approaches, to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection.