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Yayın Mathematical modeling of human facial muscles(Işık Üniversitesi, 2015-08-10) Ağdoğan, Didem; Eskil, Mustafa Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıIn this study; a new muscle model is proposed for computation of actual muscle forces that exist during facial expressions. Modeling of facial muscle forces is done on the expression of surprise. We choose this expression as it is performed by the activation of a single(frontalis) muscle. Videos used in the study are recorded with a high resolution camera where the subject's face was painted with a rectangular grid. The first and last frames of subject's surprise expression is used for extraction of muscle forces. Feature points which are located in the first frame are manually marked on the last frame with guidance of the rectangular grid. Muscle forces that are exerted on the facial skin are computed using a massspring model and an anatomical muscle model is derived. Skin is also modelled as a nonlinear deformable tissue for more realistic results. The accuracy of the proposed model is shown through simulations.Yayın An observation based muscle model for simulation of facial expressions(Elsevier Science BV, 2018-05) Erkoç, Tuğba; Ağdoğan, Didem; Eskil, Mustafa TanerThis study presents a novel facial muscle model for coding of facial expressions. We derive this model from unintrusive observation of human subjects in the progress of the surprise expression. We use a generic and single-layered face model which embeds major muscles of the human face. This model is customized onto the human subject's face on the first frame of the video. The last frame of the video is used to project a set of manually marked feature points to estimate the 3 dimensional displacements of vertices due to facial expression. Vertex displacements are used in a mass spring model to estimate the external forces, i.e. the muscle forces on the skin. We observed that the distribution of muscle forces resemble sigmoid or hyperbolic tangent functions. We chose hyperbolic tangent function as our base model and parameterized it using least squares. We compared the proposed muscle model with frequently used models in the literature.Yayın Facial expression recognition based on facial anatomy(Işık Üniversitesi, 2013-06-06) Benli, Kristin Surpuhi; Eskil, Mustafa Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Doktora ProgramıIn this thesis we propose to determine the underlying muscle forces that compose a facial expression under the constraint of facial anatomy. Muscular activities are novel features that are highly representative of facial expressions. We model human face with a 3D generic wireframe model that embeds all major muscles. The input to our expression recognition system is a video with marked set of landmark points on the first frame. We use these points and a semi-automatic fitting algorithm to register the 3D face model to the subject's face. The influence regions of facial muscles are estimated and projected to the image plane to determine feature points. These points are tracked on the image plane using optical flow algorithm. We estimate the rigid body transformation of the head through a greedy search algorithm. This stage enables us to align the 3D face model with the subject's head in consecutive frames of the video. We use ray tracing from the perspective reference point and through the image plane to estimate the new coordinates of model vertices. The estimated vertex coordinates indicate how the subject's face is deformed in the progression of an expression. The relative motion of model vertices provides us an over-determined linear system of equations where unknown parameters are the muscle activation levels. This system of equations is solved using constrained least square optimization. Muscle activity based features are evaluated in a classification problem of seven basic facial expressions. We demonstrate the representative power of muscle force based features on four classifiers; Linear Discriminant Analysis, Naive Bayes, k-Nearest Neighbor and Support Vector Machine. The best performance on the classification problem of seven expressions including neutral was 87.1 %, obtained by use of Support Vector Machine. The results we attained in this study are close to the human recognition ceiling of 87-91.7 % and comparable with the state of the art algorithms in the literature.
