Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach.

The goal of this study was to evaluate human accuracy at identifying people from static and dynamic presentations of faces and bodies. Participants matched identity in pairs of videos depicting people in motion (walking or conversing) and in "best" static images extracted from the videos. The type of information presented to observers was varied to include the face and body, the face-only, and the body-only. Identification performance was best when people viewed the face and body in motion. There was an advantage for dynamic over static stimuli, but only for conditions that included the body. Control experiments with multiple-static images indicated that some of the motion advantages we obtained were due to seeing multiple images of the person, rather than to the motion, per se. To computationally assess the contribution of different types of information for identification, we fused the identity judgments from observers in different conditions using a statistical learning algorithm trained to optimize identification accuracy. This fusion achieved perfect performance. The condition weights that resulted suggest that static displays encourage reliance on the face for recognition, whereas dynamic displays seem to direct attention more equitably across the body and face.

Learning the moves: the effect of familiarity and facial motion on person recognition across large changes in viewing format.

Familiarity with a face or person can support recognition in tasks that require generalization to novel viewing contexts. Using naturalistic viewing conditions requiring recognition of people from face or whole body gait stimuli, we investigated the effects of familiarity, facial motion, and direction of learning/test transfer on person recognition. Participants were familiarized with previously unknown people from gait videos and were tested on faces (experiment 1a) or were familiarized with faces and were tested with gait videos (experiment 1b). Recognition was more accurate when learning from the face and testing with the gait videos, than when learning from the gait videos and testing with the face. The repetition of a single stimulus, either the face or gait, produced strong recognition gains across transfer conditions. Also, the presentation of moving faces resulted in better performance than that of static faces. In experiment 2, we investigated the role of facial motion further by testing recognition with static profile images. Motion provided no benefit for recognition, indicating that structure-from-motion is an unlikely source of the motion advantage found in the first set of experiments.

Psychological and neural perspectives on the role of motion in face recognition.

In the real world, faces are in constant motion. Recently, researchers have begun to consider how facial motion affects memory for faces. The authors offer a theoretical framework that synthesizes psychological findings on memory for moving faces. Three hypotheses about the possible roles of facial motion in memory are evaluated. In general, although facial motion is helpful for recognizing familiar/famous faces, its benefits are less certain with unfamiliar faces. Importantly, the implicit social signals provided by a moving face (e.g., gaze changes, expression, and facial speech) may mediate the effects of facial motion on recognition. Insights from the developmental literature, which highlight the significance of attention in the processing of social information from faces, are also discussed. Finally, a neural systems framework that considers both the processing of socially relevant motion information and static feature-based information is presented. This neural systems model provides a useful framework for understanding the divergent psychological findings.

Recognizing moving faces: a psychological and neural synthesis.

Information for identifying a human face can be found both in the invariant structure of features and in idiosyncratic movements and gestures. When both kinds of information are available, psychological evidence indicates that: (1) dynamic information contributes more to recognition under non-optimal viewing conditions, e.g. poor illumination, low image resolution, recognition from a distance; (2) dynamic information contributes more as a viewer's experience with the face increases; and (3) a structure-from-motion analysis can make a perceptually based contribution to face recognition. A recently proposed distributed neural system for face perception, with minor modifications, can accommodate the psychological findings with moving faces.