Distinctiveness plays an important role in the recognition of faces, i.e., a distinctive face is usually easier to remember than a typical face in a recognition task. This distinctiveness effect explains why caricatures are recognized faster and more accurately than unexaggerated (i.e., veridical) faces. Furthermore, using caricatures during training can facilitate recognition of a person’s face at a later time. The objective of this thesis is to determine the extent to which photorealistic computer-generated caricatures may be used in training tools to improve recognition of faces by humans. To pursue this objective, we developed a caricaturization procedure for three-dimensional (3D) face models, and characterized face recognition performance (by humans) through a series of perceptual studies.
The first study focused on 3D shape information without texture. Namely, we tested whether exposure to caricatures during an initial familiarization phase would aid in the recognition of their veridical counterparts at a later time. We examined whether this effect would emerge with frontal rather than three-quarter views, after very brief exposure to caricatures during the learning phase and after modest rotations of faces during the recognition phase. Results indicate that, even under these difficult training conditions, people are more accurate at recognizing unaltered faces if they are first familiarized with caricatures of the faces, rather than with the unaltered faces. These preliminary findings support the use of caricatures in new training methods to improve face recognition.
In the second study, we incorporated texture into our 3D models, which allowed us to generate photorealistic renderings. In this study, we sought to determine the extent to which familiarization with caricaturized faces could also be used to reduce other-race effects (e.g., the phenomenon whereby faces from other races appear less distinct than faces from our own race). Using an old/new face recognition paradigm, Caucasian participants were first familiarized with a set of faces from multiple races, and then asked to recognize those faces among a set of confounders. Participants who were familiarized with and then asked to recognize veridical versions of the faces showed a significant other-race effect on Indian faces. In contrast, participants who were familiarized with caricaturized versions of the same faces, and then asked to recognize their veridical versions, showed no other-race effects on Indian faces. This result suggests that caricaturization may be used to help individuals focus their attention to features that are useful for recognition of other-race faces.
The third and final experiment investigated the practical application of our earlier results. Since 3D facial scans are not generally available, here we also sought to determine whether 3D reconstructions from 2D frontal images could be used for the same purpose. Using the same old/new face recognition paradigm, participants who were familiarized with reconstructed faces and then asked to recognize the ground truth versions of the faces showed a significant reduction in performance compared to the previous study. In addition, participants who were familiarized with caricatures of reconstructed versions, and then asked to recognize their corresponding ground truth versions, showed a larger reduction in performance. Our results suggest that, despite the high level of photographic realism achieved by current 3D facial reconstruction methods, additional research is needed in order to reduce reconstruction errors and capture the distinctive facial traits of an individual. These results are critical for the development of training tools based on computer-generated photorealistic caricatures from “mug shot” images.
|2011 May 1900
|Texas A and M University
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