How far did we get in face spoofing detection?

TL;DR

This paper provides a comprehensive analysis of face spoofing detection research over the past decade, highlighting trends, challenges, and future directions in the field.

Contribution

It offers a structured survey categorizing methods by descriptors and classifiers, and analyzes the evolution and performance on key public datasets.

Findings

Identifies key trends and shifts in face spoofing detection techniques.

Highlights open issues and challenges in the field.

Provides insights for future research directions.

Abstract

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.