Automatic scalable face model design for 2D model-based video coding |
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| Affiliation: | 1. Department of Biostatistics and Medical Informatics, USA;2. Vocal Tract Development Laboratory, Waisman Center, University of Wisconsin, Madison, USA;3. Department of Biomedical Engineering, National University of Singapore, Singapore;4. Department of Brain and Cognitive Sciences, Seoul National University, Republic of Korea;1. Department of Mathematics & Statistics, University of Otago, Dunedin, New Zealand;2. Institute of Fundamental Sciences—Statistics, Massey University, Palmerston North, New Zealand;1. Marine Research Institute, Spanish National Research Council, Vigo, Spain;2. Faculty of Mathematics, University of Santiago de Compostela, Spain;3. Department of Mathematics, University Jaume I, Castellón, Spain;1. Medical Physics and Radiation Protection Department, Hospital General Universitario Santa Lucía, Cartagena, Spain;2. Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, Spain;3. Diagnostic Radiology Department, Hospital General Universitario Santa Lucía, Cartagena, Spain;5. General Electric Healthcare, Universidad Politécnica de Cartagena, Spain |
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| Abstract: | Scalable low bit-rate video coding is vital for the transmission of video signals over wireless channels. A scalable model-based video coding scheme is proposed in this paper to achieve this. This paper mainly addresses automatic scalable face model design. Firstly, a robust and adaptive face segmentation method is proposed, which is based on piecewise skin-colour distributions. 43 million skin pixels from 900 images are used to train the skin-colour model, which can identify skin-colour pixels reliably under different lighting conditions. Next, reliable algorithms are proposed for detecting the eyes, mouth and chin that are used to verify the face candidatures. Then, based on the detected facial features and human face muscular distributions, a heuristic scalable face model is designed to represent the rigid and non-rigid motion of head and facial features. A novel motion estimation algorithm is proposed to estimate the object model motion hierarchically. Experimental results are provided to illustrate the performance of the proposed algorithms for facial feature detection and the accuracy of the designed scalable face model for representing face motion. |
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