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In this paper, we focus on discrete expression classification using dynamic 3D sequences (4D data) recording the facial movements. A robust approach for registering 4D data is proposed and a variant of local binary patterns on three orthogonal planes is used for feature extraction. We present a fully automatic facial expression recognition pipeline. The(More)
Textured 3D face models capture precise facial surfaces along with the associated textures, making it possible for an accurate description of facial activities. In this paper, we present a unified probabilistic framework based on a novel Bayesian Belief Network (BBN) for 3D facial expression and Action Unit (AU) recognition. The proposed BBN performs(More)
  • William A. Freed-Pastor, Hideaki Mizuno, Xi Zhao, Anita Langerød, Sung-Hwan Moon, Ruth Rodriguez-Barrueco +13 others
  • 2012
p53 is a frequent target for mutation in human tumors, and mutant p53 proteins can actively contribute to tumorigenesis. We employed a three-dimensional culture model in which nonmalignant breast epithelial cells form spheroids reminiscent of acinar structures found in vivo, whereas breast cancer cells display highly disorganized morphology. We found that(More)
— This survey focuses on discrete expression classification and facial action unit recognition performed using 3D face data, possibly including a corresponding 2D texture image. Research trends to date are summarized and the limitations of current methods are discussed. The challenges towards the development of more accurate and automated 3D facial(More)
—Automatic facial expression recognition on 3D face data is still a challenging problem. In this paper we propose a novel approach to perform expression recognition automatically and flexibly by combining a Bayesian Belief Net (BBN) and Statistical facial feature models (SFAM). A novel BBN is designed for the specific problem with our proposed parameter(More)