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In recent years, feature extraction methods make an achievement in pattern recognition and computer vision. It extracts not only useful feature for classification, but also reduces the dimension of pattern samples. In this paper, we propose orthogonal supervised spectral discriminant analysis (OSSDA) which motivated by marginal fisher analysis (MFA) and spectral clustering. It put different weights...
Facial action units (AUs) recognition is a challenging problem with many applications and is rapidly becoming an area of intense interest in research field of machine vision. Nonadditive AU combinations in which the appearance of the constituent AUs does change greatly increase the difficulties of AU recognition. Most AUs recognition methods treated each AU combination as a new AU. However, these...
How do we identify images of the same person in photo albums? How can we find images of a particular celebrity using Web image search engines? These types of tasks require solving numerous challenging issues in computer vision including: detecting whether an image contains a face, maintaining robustness to lighting, pose, occlusion, scale, and image quality, and using appropriate distance metrics...
As confirmed by recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Apart from the mere computation of the visual motion of the viewed objects, the motion itself conveys far more information, which helps understanding the scene. This paper provides an overview and some new insights on the use of dynamic...
This paper describes a method to perform face pose estimation and high resolution facial feature extraction on the basis of stereoscopic color images. Unlike other approaches no light projection is required at running time. In our method face detection is based on color driven clustering of 3D points derived from stereo. A mesh model is registered with the post-processed face cluster using a variant...
We present a novel real time multi camera system for tracking 3D position of a face in an office environment. The system uses a combination of low level and high level features for robust tracking of a face in two dimensional (2D) view of each camera. Then the 2D estimates are combined through Quality Threshold clustering to produce an estimate of the 3D position, which is fed back to the 2D trackers...
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