Showing papers by "Matthew Turk published in 2003"

Manifold of facial expression

Abstract: We propose the concept of manifold of facial expression based on the observation that images of a subject's facial expressions define a smooth manifold in the high dimensional image space. Such a manifold representation can provide a unified framework for facial expression analysis. We first apply active wavelet networks (AWN) on the image sequences for facial feature localization. To learn the structure of the manifold in the feature space derived by AWN, we investigated two types of embeddings from a high dimensional space to a low dimensional space: locally linear embedding (LLE) and Lipschitz embedding. Our experiments show that LLE is suitable for visualizing expression manifolds. After applying Lipschitz embedding, the expression manifold can be approximately considered as a super-spherical surface in the embedding space. For manifolds derived from different subjects, we propose a nonlinear alignment algorithm that keeps the semantic similarity of facial expression from different subjects on one generalized manifold. We also show that nonlinear alignment outperforms linear alignment in expression classification.

The Postural Comfort Zone for Reaching Gestures

Abstract: We have proposed a method for objective assessment of postural comfort (Kolsch et al, 2003) We defined comfort as the range of postures that is voluntarily assumed despite the availability of other postures Designing user interfaces within the limits of comfort zones can avert risks associated with unknown alternative use patters of the interfaceHere we report on a user study that investigated the comfort zone for free-hand gestures in the horizontal plane at about stomach height This space is of particular interest to novel technologies such as gesture recognition and virtual reality The results are in line with previous studies on postural discomfort, but improve on resolution and are not based on subjective, questionnaire-based data acquisition This study also serves as an example for how to design studies for comfort evaluation

Real-time view-based face alignment using active wavelet networks

Abstract: The active wavelet network (AWN) [C. Hu et al., (2003)] approach was recently proposed for automatic face alignment, showing advantages over active appearance models (AAM), such as more robustness against partial occlusions and illumination changes. We (1) extend the AWN method to a view-based approach, (2) verify the robustness of our algorithm with respect to unseen views in a large dataset and (3) show that using only nine wavelets, our method yields similar performance to state-of-the-art face alignment systems, with a significant enhancement in terms of speed. After optimization, our system requires only 3 ms per iteration on a 1.6 GHz Pentium IV. We show applications in face alignment for recognition and real-time facial feature tracking under large pose variations.

Active Wavelet Networks for Face Alignment.

Abstract: The active appearance model (AAM) algorithm has proved to be a successful method for face alignment and synthesis. By elegantly combining both shape and texture models, AAM allows fast and robust deformable image matching. However, the method is sensitive to partial occlusions and illumination changes. In such cases, the PCA-based texture model causes the reconstruction error to be globally spread over the image. In this paper, we propose a new method for face alignment called active wavelet networks (AWN), which replaces the AAM texture model by a wavelet network representation. Since we consider spatially localized wavelets for modeling texture, our method shows more robustness against partial occlusions and some illumination changes.

An Objective Measure for Postural Comfort

Abstract: Biomechanics determines the physical range in which humans can move their bodies. Human factors research delineates a subspace in which humans can operate without experiencing musculoskeletal strai...