| F. de la Torre, Automatic learning of appearance face models, in: Second Internat. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, 2001, pp. 32--39. |
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F. de la Torre, Automatic learning of appearance face models, in: Second Internat. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, 2001, pp. 32--39.
No context found.
F. de la Torre, Automatic learning of appearance face models, in: Second Internat. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, 2001, pp. 32--39.
....use this factorized basis to update some characteristics to personalize a model. In this paper, we will apply Robust Parameterized Component Analysis to learn a PSFAM and will illustrate the method with applications involving facial modeling. Preliminary results of this paper were presented in [12]. 2 Previous Work This paper is related to previous work on subspace learning methods and PCA. It is beyond the scope of the paper to review all possible applications of PCA, therefore we just briefly describe the theory and point to related work for further information. 2.1 Subspace Learning ....
....parameter. 4.2 Robust Deterministic Learning The previous section describes a method for computing an initial estimate of the parametersB C, A. In order to improve the solution and achieve sub pixel accuracy, a normalized gradient descent algorithm for minimizing Eq. 4) has been employed in [12]. Alternatively (and conveniently) we can reformulate the minimization problem as one of iteratively reweighted least squares (IRLS) which provides an approximate, iterative, solution to the robust M estimation problem [28] For a given #,a matrix W , which contains the positive weights for ....
F. de la Torre. Automatic learning of appearance face models. In Second International Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, pages 32--39, 2001.
....faces of two different people performing similar facial expressions. In general it is hard to model and animate faces, even when they are cartoons characters. Usually complex models encoding the physical underlying musculature of the face are used (e.g. Candide model [1, 10] Recently De la Torre [6] has used eigenfeatures [15, 17] to automatically learn person speci c appearance face models and dynamically animate them. The face is modeled using separate eigenfeatures since facial features such as the eyes and mouth undergo almost independent changes in appearance [6, 17] The facial ....
....Recently De la Torre [6] has used eigenfeatures [15, 17] to automatically learn person speci c appearance face models and dynamically animate them. The face is modeled using separate eigenfeatures since facial features such as the eyes and mouth undergo almost independent changes in appearance [6, 17]. The facial feature appearance models are automatically learned from an input image sequence given the starting regions in the rst frame (Figure 2) 6] Given two sets of faces of dimensions ( d 2 21128 1 and d 2 27858 1 ) we manually select all pairs of corresponding images and store ....
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F. De la Torre. Automatic learning of appearance face models. Second Int. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Realtime Systems, 2001.
No context found.
F. De la Torre. Automatic learning of appearance face models. In Proceedings of the Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Realtime Systems, pages 32--39, 2001.
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F. D. la Torre. Automatic learning of appearance face models. In Recognition, Analysis and Tracking of Faces and Gestures in Realtime Systems, pages 32--39, 2001.
No context found.
F. De la Torre. Automatic learning of appearance face models. In Proceedings of the Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Realtime Systems, pages 32--39, 2001.
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