Multi-view face segmentation using fusion of statistical shape and appearance models |
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| Affiliation: | 1. Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain;2. Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain;3. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain;1. Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt;2. University of California, Davis, USA;3. College of Information and Electrical Engineering, Asia University, Taiwan;4. Department of Information Technology, Faculty of Computers & Artificial Intelligence, Cairo University, Cairo 12613, Egypt;1. School of Information Engineering, China University of Geosciences, Wuhan, China;2. Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA;3. Wuhan Huazhong Numerical Control Co., Ltd., China;1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China;2. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China |
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| Abstract: | This paper demonstrates how a weighted fusion of multiple Active Shape (ASM) or Active Appearance (AAM) models can be utilized to perform multi-view facial segmentation with only a limited number of views available for training the models. The idea is to construct models only from frontal and profile views and subsequently fuse these models with adequate weights to segment any facial view. This reduces the problem of multi-view facial segmentation to that of weight estimation, the algorithm for which is proposed as well. The evaluation is performed on a set of 280 landmarked static face images corresponding to seven different rotation angles and on several video sequences of the AV@CAR database. The evaluation demonstrates that the estimation of the weights does not have to be very accurate in the case of ASM, while in the case of AAM the influence of correct weight estimation is more critical. The segmentation with the proposed weight estimation method produced accurate segmentations in 91% of 280 testing images with the median point-to-point error varying from two to eight pixels (1.8–7.2% of average inter-eye distance). |
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