The movement advantage in famous and unfamiliar faces: a comparison of point-light displays and shape-normalised avatar stimuli.

Facial movement may provide cues to identity, by supporting the extraction of face shape information via structure-from-motion, or via characteristic patterns of movement. Currently, it is unclear whether familiar and unfamiliar faces derive the same benefit from these mechanisms. This study examined the movement advantage by asking participants to match moving and static images of famous and unfamiliar faces to facial point-light displays (PLDs) or shape-normalised avatars in a same/different task (experiment 1). In experiment 2 we also used a same/different task, but participants matched from PLD to PLD or from avatar to avatar. In both experiments, unfamiliar face matching was more accurate for PLDs than for avatars, but there was no effect of stimulus type on famous faces. In experiment 1, there was no movement advantage, but in experiment 2, there was a significant movement advantage for famous and unfamiliar faces. There was no evidence that familiarity increased the movement advantage. For unfamiliar faces, results suggest that participants were relying on characteristic movement patterns to match the faces, and did not derive any extra benefit from the structure-from-motion cues in the PLDs. The results indicate that participants may use static and movement-based cues in a flexible manner when matching famous and unfamiliar faces.

Real-time Avatar Animation from a Single Image.

A real time facial puppetry system is presented. Compared with existing systems, the proposed method requires no special hardware, runs in real time (23 frames-per-second), and requires only a single image of the avatar and user. The user's facial expression is captured through a real-time 3D non-rigid tracking system. Expression transfer is achieved by combining a generic expression model with synthetically generated examples that better capture person specific characteristics. Performance of the system is evaluated on avatars of real people as well as masks and cartoon characters.

Non-rigid Face Tracking with Local Appearance Consistency Constraint.

In this paper we present a new discriminative approach to achieve consistent and efficient tracking of non-rigid object motion, such as facial expressions. By utilizing both spatial and temporal appearance coherence at the patch level, the proposed approach can reduce ambiguity and increase accuracy. Recent research demonstrates that feature based approaches, such as constrained local models (CLMs), can achieve good performance in non-rigid object alignment/tracking using local region descriptors and a non-rigid shape prior. However, the matching performance of the learned generic patch experts is susceptible to local appearance ambiguity. Since there is no motion continuity constraint between neighboring frames of the same sequence, the resultant object alignment might not be consistent from frame to frame and the motion field is not temporally smooth. In this paper, we extend the CLM method into the spatio-temporal domain by enforcing the appearance consistency constraint of each local patch between neighboring frames. More importantly, we show that the global warp update can be optimized jointly in an efficient manner using convex quadratic fitting. Finally, we demonstrate that our approach receives improved performance for the task of non-rigid facial motion tracking on the videos of clinical patients.

Deformable Face Fitting with Soft Correspondence Constraints.

Despite significant progress in deformable model fitting over the last decade, the problem of efficient and accurate person-independent face fitting remains a challenging problem. In this work, a reformulation of the generative fitting objective is presented, where only soft correspondences between the model and the image are enforced. This has the dual effect of improving robustness to unseen faces as well as affording fitting time which scales linearly with the model's complexity. This approach is compared with three state-of-the-art fitting methods on the problem of person independent face fitting, where it is shown to closely approach the accuracy of the currently best performing method while affording significant computational savings.