Evaluation of Face Recognition Algorithms on Avatar Face Datasets

Abstract

Art metrics, a field of study that identifies, classifies and authenticates virtual reality avatars and intelligent software agents, has been proposed as a tool for fighting crimes taking place in virtual reality communities and in multiplayer game worlds. Forensic investigators are interested in developing tools for accurate and automated tracking and recognition of avatar faces. In this paper, we evaluate state of the art academic and commercial algorithms developed for human face recognition in the new domain of avatar recognition. While the obtained results are encouraging, ranging from 53.57% to 79.9% on different systems, the paper clearly demonstrated that there is room for improvement and presents avatar face recognition as an open problem to the pattern recognition and biometric communities.