1 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 1 / 1
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.
