Analysis and Modeling of Faces and Gestures, IEEE International Workshop on (2003)
Oct. 17, 2003 to Oct. 17, 2003
D. A. Roark , University of Texas at Dallas
A. J. O'Toole , University of Texas at Dallas
H. Abdi , University of Texas at Dallas
Understanding the human performance factors that mediate successful person identification can be helpful in the development of automatic face recognition algorithms. Face familiarity and facial motion are two factors that seem especially useful when subjects make recognition decisions from challenging viewing formats. We tested the effects of these two factors on person recognition from naturalistic, surveillance-like video. Subjects learned faces from either static photographs or facial speech videos and were asked to recognize people from whole body gait videos. We found that the more experience participants had with a face during learning (i.e., 1-view, 2-view, and 4-view conditions), the better their recognition performance for people in the whole body video gait clips. Thus, familiarizing subjects with high-resolution images or videos of faces was sufficient to improve recognition from low-resolution, whole-body images. Moreover, participants who learned faces from dynamic video clips were more accurate than participants who learned the faces from static images, but only when they were familiar with the faces. Facial motion and face familiarity may therefore play a role in understanding recognition when there are photometric inconsistencies between learning and test stimuli.
H. Abdi, D. A. Roark and A. J. O'Toole, "Human Recognition of Familiar and Unfamiliar People in Naturalistic Video," 2003 IEEE International Workshop on Analysis and Modeling of Faces and Gestures(AMFG), Nice, France, 2003, pp. 36.