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This paper presents robust facial pose estimation techniques based on the underlying low dimensional manifolds embedded in facial images of varying pose. In our approach, facial feature points of training faces are converted to a low dimensional projection space to form a smooth manifold surface. Subsequent faces are automatically detected, facial feature points are extracted and mapped onto the low...
Motivated by the non-linear manifold learning ability of the kernel principal component analysis (KPCA), we propose in this paper a method for detecting human postures from single images by employing KPCA to learn the manifold span of a set of HOG features that can effectively represent the postures. The main contribution of this paper is to apply the KPCA as a non-linear learning and open-set classification...
Locally linear embedding (LLE) is a prevalent manifold learning method in pattern recognition and machine learning. It preserves the intrinsic structure information of data set and has been widely applied to feature extraction and dimensionality reduction. This paper introduces LLE to aircraft pose recognition. The representative motion poses of an aircraft in the air are analyzed. Unfolding results...
In this paper we present a novel appearance based approach to the problem of face pose classification. This method suggests the subject-independent pose classification of face images using bilateral filtering and wavelet transform as preprocessing and isometric projection based subspace learning for the extracting of discriminant feature vectors. Our proposed method is evaluated on a large image set...
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