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This paper describes two novel facial expression recognition methods which are robust for head rotation within a certain angle range between -30 degrees and +30 degrees. We had proposed Eigenspace Method based on Class features of object (EMC) and Multiple Discriminant Analysis (MDA) for facial expression recognition. Our new methods, pEMC (parametric Eigenspace Method based on Class Features) and...
The performance of face recognition systems depends on conditions being consistent, including lighting, pose and facial expression. To solve the problem produced by pose variation it is suggested to pre-estimate the pose orientation of the given head image before it is recognized. In this paper, we propose a head pose estimation method that is an improvement on the one proposed by N. Hu et al. The...
Principal component analysis (PCA) and Fisher discriminate analysis (FDA) of holistic approach of Information theory have been analyzed. Two steps for recognition are taken: training and testing. In the training phase a set of the eigenvectors of the covariance matrix of the images used for training. These eigenvectors are also called as eigenfaces. In testing phase when a new input image is given...
In this paper, we present some extensions to Active Appearance Model and its extended view-based models are applied for tracking faces through wide angles. The extensions include:(1) a new multiband representation created by filtering the images with a set of orthogonal spatial filters and then applying a non-linear normalization to them, and (2)adding extra landmarks outside the facial region to...
It has been promising to provide personalized services for improving our living environment in support of information technologies. Sitting is one of the natural actions in our daily life. We focus on sitting behavior as a cue for providing such services. We used a pressure sensor seat on a chair for identifying sitting postures. In the experiments, we classified nine postures, including leaning forward...
We present a discriminative approach to frame-by-frame head pose tracking that is robust to a wide range of illuminations and facial appearances and that is inherently immune to accuracy drift. Most previous research on head pose tracking has been validated on test datasets spanning only a small (< 20) subjects under controlled illumination conditions on continuous video sequences. In contrast,...
Automatic localization of 3D facial features is important for face recognition, tracking, modeling and expression analysis. Methods developed for 2D images were shown to have problems working across databases acquired with different illumination conditions. Expression variations, pose variations and occlusions also hamper accurate detection of landmarks. In this paper we assess a fully automatic 3D...
Face recognition in the presence of pose changes remains a largely unsolved problem. Severe pose changes, resulting in dramatically different appearances, is one of the main difficulties. We present a support vector machine (SVM) based system that learns the relations between corresponding local regions of the face in different poses as well as a simple SVM based system for automatic alignment of...
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