Head pose estimation using Fisher Manifold learning

Chen, L.; Zhang, L.; Hu, Yuxiao; Li, M.; Zhang, H.

doi:10.1109/amfg.2003.1240844

Cited by 50 publications

(41 citation statements)

References 13 publications

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“…Chen et al [22] uses the face images of two specific head poses and estimates the head poses between them through classification-based nonlinear interpolation. This approach is based on the assumption that the face images of multiple view lie on a manifold in the original image feature space.…”

Section: Manifold Learningmentioning

confidence: 99%

Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning

Wang

Huang

Gao

et al. 2008

Lecture Notes in Computer Science

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Abstract. Head pose estimation is an important task for many face analysis applications, such as face recognition systems and human computer interactions. In this paper we aim to address the pose estimation problem under some challenging conditions, e.g., from a single image, large pose variation, and un-even illumination conditions. The approach we developed combines non-linear dimension reduction techniques with a learned distance metric transformation. The learned distance metric provides better intra-class clustering, therefore preserving a smooth lowdimensional manifold in the presence of large variation in the input images due to illumination changes. Experiments show that our method improves the performance, achieving accuracy within 2-3 degrees for face images with varying poses and within 3-4 degrees error for face images with varying pose and illumination changes.

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Section: Manifold Learningmentioning

confidence: 99%

Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning

Wang

Huang

Gao

et al. 2008

Lecture Notes in Computer Science

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“…Kernel machine: KPCA and KDA In [11] the use of KPCA for modeling the multi-view faces in the original image space was presented. Assuming data non-linearly distributed, we can map it onto a new higher dimensional feature space {Φ(x) ∈ F} where the data possess a linear property.…”

Section: Subspace Projectionmentioning

confidence: 99%

“…This study indicates that identity-independent pose can be discriminated by prototype matching with suitable filters. Some efforts have been put to investigate the 2D pose estimation problem [5] [6][11] [13] [14] and they are mainly focused on the use of statistical learning techniques, such as SVC in [5], KPCA in [11], multi-view eigen-space in [14], eigen-space from best Gabor filter in [13], manifold learning in [6] etc. All these algorithms are based on the features from entire faces.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…Pose can only be determined in certain degrees of freedom (DOF), instead of the full 6 DOF as the 3D one does. 2D pose estimation can be used as the front-end for multi-view face analysis [5] [11]; as well as to provide the initial reference frame for 3D head pose tracking. In [12], the author investigated the dissimilarity between poses by using some specific filters such as Gabor filters and PCA.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

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An Integrated Two-Stage Framework for Robust Head Pose Estimation

Trivedi

2005

Lecture Notes in Computer Science

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Abstract. Subspace analysis has been widely used for head pose estimation. However, such techniques are usually sensitive to data alignment and background noise. In this paper a two-stage approach is proposed to address this issue by combining the subspace analysis together with the topography method. The first stage is based on the subspace analysis of Gabor wavelets responses. Different subspace techniques were compared for better exploring the underlying data structure. Nearest prototype matching using Euclidean distance was used to get the pose estimate. The single pose estimated was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the uncertainty is eliminated by analyzing finer geometrical structure details captured by bunch graphs. This coarse-tofine framework was evaluated with a large data set. We examined 86 poses, with the pan angle spanning from −90 o to 90 o and the tilt angle spanning from −60 o to 45 o . The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone.

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“…Estimation of facial poses from video sequences is important for both computer vision and multimedia content analysis, such as scene understanding, event estimation, etc., or activity analysis in video surveillances. 1,2 Over the past decades, facial pose estimation remains an active research area in which a range of techniques has been reported to investigate the pose-estimation problem, such as support vector classification, 3 eigenspace from Gabor filters, 4 manifold learning, 5 independent component analysis, 6 and a two-stage framework based on Gabor wavelets, bunch graphs, 7 etc. Recently, mutual information ͑MI͒ is used to extract facial poses from video sequences.…”

Section: Introductionmentioning

confidence: 99%

Empirical mode decomposition-based facial pose estimation inside video sequences

2010

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Abstract. We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition ͑EMD͒ and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function ͑IMF͒ components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions.

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Head pose estimation using Fisher Manifold learning

Cited by 50 publications

References 13 publications

Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning

Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning

An Integrated Two-Stage Framework for Robust Head Pose Estimation

Empirical mode decomposition-based facial pose estimation inside video sequences

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