Local binary patterns for multi-view facial expression recognition
Computer Vision and Image Understanding
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This paper introduces a method for pose-invariant facial affect analysis and a real-time system for facial affect analysis using this method. The method is centered on developing a feature vector that is more robust to rigid body movements while retaining information important to facial affect analysis. This feature vector is produced using thin-plate splines to extract affine transformations independently from nonlinear transformations quickly and efficiently. The affine portion can be used to describe the rigid body motion because planar motions in a perspective projection can be approximated by an affine transformation. Removing the affine portion and using the nonlinear portion of the thin-plate spline warping provides information on the nonlinear motion caused by facial affects. The real-time system developed using this method is composed of three main components: facial landmark tracking, feature vector extraction, and affect classification. The system processes streaming video in real-time. Testing was performed to examine the invariance to rotation as well as subject independence of the system. Finally its application in real-world environments is discussed.