Baseline avatar face detection using an extended set of Haar-like features
It is desirable to address accessibility issues within virtual worlds. Moreover, curbing criminal activities within virtual worlds is a major concern to the law enforcement agencies. Forensic investigators and accessibility researchers are gaining considerable interests in detecting and tracking avatars as well as describing their appearance within virtual worlds. Leinhart and Maydt have introduced a novel-set of Haar like features by extending the Viola Jones approach towards rapid object detection. We test this Haar cascade on human and avatar faces. Accuracy rates of 79% on human and 74% on avatar faces are obtained. The goal is to detect avatar faces in upright frontal face datasets and establish a baseline for future work in computer generated face recognition.