Face Recognition in the Virtual World: Recognizing Avatar Faces

Abstract

Criminal activity in virtual worlds is becoming a major problem for law enforcement agencies. Forensic investigators are becoming interested in being able to accurately and automatically track people in virtual communities. In this paper a set of algorithms capable of verification and recognition of avatar faces with high degree of accuracy are described. Results of experiments aimed at within-virtual-world avatar authentication and inter-reality-based scenarios of tracking a person between real and virtual worlds are reported. In the FERET-to-Avatar face dataset, where an avatar face was generated from every photo in the FERET database, a COTS FR algorithm achieved a near perfect 99.58% accuracy on 725 subjects. On a dataset of avatars from Second Life, the proposed avatar-to-avatar matching algorithm (which uses a fusion of local structural and appearance descriptors) achieved average true accept rates of (i) 96.33% using manual eye detection, and (ii) 86.5% in a fully automated mode at a false accept rate of 1.0%. A combination of the proposed face matcher and a state-of-the art commercial matcher (FaceVACS) resulted in further improvement on the inter-reality-based scenario.