T. Vetter. Learning novel views to a single face image. In 2 International Conference on Automatic Face and Gesture Recognition 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Invariance, Face Recognition, Segmentation. I. Introduction M ODELS capable of synthesising complete images of objects have over the past few years proven their worth when interpreting unseen images. Applications include real time tracking of deformable objects [1] 2] face recognition [3], 4] 5] and recovery of anatomical structures in magnetic resonance images [6] 7] 8] 9] ultrasound images [10] and x rays [9] 11] The key idea to all of these generative models is to perform a per pixel comparison between unseen input images and synthesised images and subsequently ....

T. Vetter, \Learning novel views to a single face image," Automatic Face and Gesture Recognition, 1996.

....combinations of steerable basis flow fields. These linear models constrain the interpretation of image motion, and are used in the same way as translational or affine motion models. Similar approaches have been used in modeling the deformations between individual faces in a database of face images [6, 17, 23, 33, 40, 41]. Generalizing brightness constancy. Much of the recent work in motion estimation has focused on achieving increased robustness in the presence of unmodeled appearance changes. In this paper we take the approach of explicitly modeling many of these events and hence extend the notion of ....

T. Vetter, Learning novel views to a single face image, in International Conference on Automatic Face and Gesture Recognition, Killington, VT, 1996, pp. 22--27.

....in which the image is first registered using 2D affine transformations and then, in a second step, the optical flow is computed and a 2D warp is performed on the output of the flow computation. Finally, pose invariance can be achieved by applying techniques similar to the one described in [15]. The 2D shape and texture of the probe image are decomposed into example 2D shapes and textures. This results in a representation which is invariant under any 3D affine transformation. A WWW demonstration of the robust correlation can be reached at ....

T. Vetter. Learning novel views to a single face image. In FG'96, volume 1, pages 22--27, Los Alamitos, CA, USA, 1996. Computer Society Press.

....face orientation has received deliberate attention. The FERET data base [17] for instance, includes both frontal and oblique views, and several specialized data bases have been collected to try to develop methods of face recognition that are invariant to moderate change in face orientation [20]. In the face expression literature, use of multiple perspectives is rare and relatively less attention has been focused on the problem of pose invariance. Most researchers assume that face orientation is limited to in plane variation [1] or that out of plane variation is small [10, 11] In ....

T. Vetter, Learning novels views to a single face image. Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition `95: 22-29, 1995.

....combinations of steerable basis flow fields. These linear models constrain the interpretation of image motion, and are used in the same way as translational or affine motion models. Similar approaches have been used in modeling the deformations between individual faces in a database of face images [6, 17, 23, 33, 40, 41]. Generalizing Brightness Constancy Much of the recent work in motion estimation has focused on achieving increased robustness in the presence of unmodeled appearance changes. In this paper we take the approach of explicitly modeling many of these events and hence extend the notion of ....

T. Vetter. Learning novel views to a single face image. In International Conference on Automatic Face and Gesture Recognition, pages 22--27, Killington, Vermont, 1996.

.... model to warp images to match with the initial frame (Black Yacoob, 1985; Wu, Kanade, Cohn, Li, 1998) For larger rotations, however, higher degree motion models or multiple camera setups may be needed (Basu, Essa, Pentland, 1996; DeCarlo Metaxis, 1995; Narayanan, Rander, Kanade, 1998; Vetter, 1995). Multiple camera setups already are common in observational research, so the necessary recording capability is present in many laboratories. The present analyses focused on the concordance between Automated Face Analysis and manual FACS coding in classifying action units and action unit ....

Vetter, T. (1995). Learning novels views to a single face image. Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition `95, 22-29.

....coefficients of the model for subsequent recognition interpretation of the image motion. 2 Related Work Much of the recent work on learning parameterized models of image deformation has occurred in the face recognition literature to model the deformations between the faces of different people [3, 9, 11, 13, 18]. Correspondences between different faces were obtained either by hand or by an optical flow method, and were then used to learn a lowerdimensional model. In some cases this involved learning the parameters of a physically based deformable object [13] In other cases a basis set of deformation ....

T. Vetter. Learning novel views to a single face image. Int. Conf. Auto. Face & Gesture Recog., pp. 22-- 27, 1996.

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T. Vetter. Learning novel views to a single face image. In 2 International Conference on Automatic Face and Gesture Recognition 1997.

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T. Vetter, "Learning novel views to a single face image," in 2 nd International Conference on Automatic Face and Gesture Recognition 1997, pp. 22--27, IEEE Computer Society Press, (Los Alamitos, California), Oct. 1996.

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T. Vetter. Learning novel views to a single face image. In 2 on Automatic Face and Gesture Recognition 1997.

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T. Vetter. Learning novel views to a single face image. In 2 ############# ####### ############### #### ### ################## ####, pages 22-27, Los Alamitos, California, Oct. 1996. IEEE Computer Society Press.

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