Analyzing the Scientific Evolution of Face Recognition Research and Its Prominent Subfields

Abstract

This paper presents a science mapping approach to analyze thematic evolution of face recognition research. For this reason, different bibliometric tools are combined (performance analysis, science mapping and Co-word analysis) in order to identify the most important, productive and the highest-impact subfields. Moreover, different visualization tools are used to display a graphical vision of face recognition field to determine the thematic domains and their evolutionary behavior. Finally, this study proposes the most relevant lines of research for the face recognition field. Findings indicate a huge increase in face recognition research since 2014. Mixed approaches revealed a great interest compared to local and global approaches. In terms of algorithms, the use of deep learning methods is the new trend. On the other hand, the illumination variation impact on face recognition algorithms performances is nowadays, the most important and impacting challenge for the face recognition field.