Authors: Chunlong Hu Liyu Gong Tianjiang Wang Fang Liu Qi Feng
Publish Date: 2013/08/29
Volume: 73, Issue: 3, Pages: 1863-1884
Abstract
The accuracy of head pose estimation is significant for many computer vision applications such as face recognition driver attention detection and humancomputer interaction Most appearancebased head pose estimation works typically extract the lowdimensional face appearance features in some statistic subspaces where the subspaces represent the underlying geometry structure of the pose space However there is an open problem namely how to effectively represent appearancebased subspace face for the head pose estimation problem To address the problem this paper proposes a head pose estimation approach based on the Lie Algebrized Gaussians LAG feature to model the pose characteristic LAG is built on Gaussian Mixture Models GMM which actually not only models the distribution of local appearance features but also captures the Lie group manifold structure of the feature space Moreover to keep multiresolution structure information LAG is operated on many subregions of the image As a result these properties of LAG enable it to effectively model the structure of subspace face which can lead to powerful discriminative ability for head pose estimation After representing subspace face using the LAG we treat the head pose estimation as a classification problem The withinclass covariance normalization WCCN based Support Vector Machine SVM classifier is employed to achieve robust performance as WCCN could reduce the withinclass variabilities of the same pose Extensive experimental analysis and comparison with both traditional and stateoftheart algorithms on two challenging benchmarks demonstrate the effectiveness of our approachThank the editors and the anonymous referees for their valuable comments This work was supported by the National Natural Science Foundation of China under grant number 61073094 and U1233119 The authors would also like to thank Xinwei Jiang for his help
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