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Biased Manifold Embedding: A Framework for Person-Independent Head Pose Estimation

11 years 21 days ago
Biased Manifold Embedding: A Framework for Person-Independent Head Pose Estimation
The estimation of head pose angle from face images is an integral component of face recognition systems, human computer interfaces and other human-centered computing applications. To determine the head pose, face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional feature space. While manifold learning techniques capture the geometrical relationship between data points in the highdimensional image feature space, the pose label information of the training data samples are neglected in the computation of these embeddings. In this paper, we propose a novel supervised approach to manifold-based non-linear dimensionality reduction for head pose estimation. The Biased Manifold Embedding (BME) framework is pivoted on the ideology of using the pose angle information of the face images to compute a biased neighborhood of each point in the feature space, before determining the low-dimensional embedding. The proposed BME approach...
Vineeth Nallure Balasubramanian, Jieping Ye, Sethu
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Vineeth Nallure Balasubramanian, Jieping Ye, Sethuraman Panchanathan
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