Query Driven Localized Linear Discriminant Models for Head Pose Estimation
Zhu Li,Yun Fu,Junsong Yuan,Thomas S. Huang,Ying Wu +4 more
- 02 Jul 2007
- pp 1810-1813
TL;DR: This work develops a query point driven, localized linear subspace learning method that approximates the non-linearity of the head pose manifold structure with piece-wise linear discriminating subspaces/metrics.
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Abstract: Head pose appearances under the pan and tilt variations span a high dimensional manifold that has complex structures and local variations. For pose estimation purpose, we need to discover the subspace structure of the manifold and learn discriminative subspaces/metrics for head pose recognition. The performance of the head pose estimation is heavily dependent on the accuracy of structure learnt and the discriminating power of the metric. In this work we develop a query point driven, localized linear subspace learning method that approximates the non-linearity of the head pose manifold structure with piece-wise linear discriminating subspaces/metrics. Simulation results demonstrate the effectiveness of the proposed solution in both accuracy and computational efficiency.
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Citations
Head Pose Estimation in Computer Vision: A Survey
TL;DR: This paper discusses the inherent difficulties in head pose estimation and presents an organized survey describing the evolution of the field, comparing systems by focusing on their ability to estimate coarse and fine head pose and highlighting approaches well suited for unconstrained environments.
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Head Pose Estimation Based on Multivariate Label Distribution
TL;DR: Labeling the images with MLD can not only alleviate the problem of inaccurate pose labels, but also boost the training examples associated to each pose without actually increasing the total amount of training examples.
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Subspaces Indexing Model on Grassmann Manifold for Image Search
Xinchao Wang,Zhu Li,Dacheng Tao +2 more
TL;DR: A novel local subspace indexing model for image search termed Subspace Indexing Model on Grassmann Manifold (SIM-GM), which is able to deal with a large number of training samples efficiently and return an effective local space model, so the recognition performance could be significantly improved.
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
TL;DR: In this article, a mixture of linear regressions with partially-latent output is used to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena.
88
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