| Paper: | MLSP-P5.9 | ||
| Session: | Image and Video Processing | ||
| Time: | Thursday, May 20, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Image and Video Processing Applications | ||
| Title: | APPEARANCE MODEL BASED FACE-TO-FACE TRANSFORM | ||
| Authors: | Takayuki Nagai; The University of Electro-Communications | ||
| Truong Nguyen; University of California, San Diego | |||
| Abstract: | In this paper, a novel approach to face-to-face transform is presented. The face-to-face transform is a technique, which transforms one person's facial actions to the others. In general, the 3D models of faces are used for such transformation. Therefore the facial action parameters should be estimated from the 2D input images, which is not an easy task. On the contraly, our proposed approach is based on the 2D appearance model instead of the 3D model so that the model is acquired by learning directly from training images. To achieve this, we investigate making use of the Hidden Markov Model (HMM) framework, which models the correspondence between an input face and the other's one as well as the appearances of both faces. The experimental results show the effectiveness of the proposed method. | ||
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