Supervised local subspace learning for continuous head pose estimation

Huang, Dong; Storer, Markus; Torre, Fernando De la; Bischof, Horst

doi:10.1109/cvpr.2011.5995683

Cited by 70 publications

(49 citation statements)

References 20 publications

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“…• or 30 • , as is often the case, the regression estimation will tend more towards these values [7]. This makes the training process difficult, as natural data at bigger angles are difficult to obtain.…”

Section: Synthetic Images For Wvrmentioning

confidence: 97%

“…These results are based on the database FacePix containing only 30 different subjects. Overfitting and non-uniformly distributed data are two of the core problems of regression [7]. They can be avoided by using synthetically rendered images with infinitely many possible subjects, generated in this work with the 3D Morphable Model of the working group of T. Vetter [8], [9].…”

Section: Introductionmentioning

confidence: 99%

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Wavelet Reduced Support Vector Regression for Efficient and Robust Head Pose Estimation

Rätsch

Quick

Huber

et al. 2012

2012 Ninth Conference on Computer and Robot Vision

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Abstract-In this paper, we introduce concepts to reduce the computational complexity of regression, which are successfully used for Support Vector Machines. To the best of our knowledge, we are the first to publish the use of a cascaded Reduced Set Vector approach for regression. The WaveletApproximated Reduced Vector Machine classifiers for face and facial feature point detection are extended to regression for efficient and robust head pose estimation. We use synthetic data, generated by the 3D Morphable Model, for optimal training sets and demonstrate results superior to state-ofthe-art techniques. The new Wavelet Reduced Vector Regression shows similarly good results on natural data, gaining a reduction of the complexity by a factor of up to 560. The introduced Evolutionary Regression Tree uses coarse-tofine loops of strongly reduced regression and classification up to most accurate complex machines. We demonstrate the Cascaded Condensation Tracking for head pose estimation for a large pose range up to ±90 degrees on videostreams.

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Section: Synthetic Images For Wvrmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Wavelet Reduced Support Vector Regression for Efficient and Robust Head Pose Estimation

Rätsch

Quick

Huber

et al. 2012

2012 Ninth Conference on Computer and Robot Vision

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show abstract

“…Huang et al [14] present Supervised Local Subspace Learning (SL2), a method that learns a local linear model from a sparse and non-uniformly sampled training set. SL2 learns a mixture of local tangent spaces that is robust to under-sampled regions, and due to its regularization properties it is also robust to over-fitting.…”

Section: The Appearance-based Approachmentioning

confidence: 99%

Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix

Kim

Lee

Sohn

et al. 2014

Hum. Cent. Comput. Inf. Sci.

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In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, two techniques were employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix is combination of Binary Pattern and a Run Length matrix. The binary pattern was calculated by randomly selected operator. In order to extract feature of training patch, we calculate statistical texture features from the Binary Pattern Run Length matrix. Moreover we perform some techniques to real-time operation, such as control the number of binary test. Experimental results show that our algorithm is efficient and robust against illumination change.

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“…The most successful methods for monocular head pose estimation are those using nonlinear regression [8,15]. Work in this area include neural networks with locally linear maps [18] and multilayer perceptrons [23], in addition to support vector machine regression after PCA projection [12].…”

Section: Introductionmentioning

confidence: 99%

On partial least squares in head pose estimation: How to simultaneously deal with misalignment

Haj

Gonzàlez

Davis

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors.

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Supervised local subspace learning for continuous head pose estimation

Cited by 70 publications

References 20 publications

Wavelet Reduced Support Vector Regression for Efficient and Robust Head Pose Estimation

Wavelet Reduced Support Vector Regression for Efficient and Robust Head Pose Estimation

Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix

On partial least squares in head pose estimation: How to simultaneously deal with misalignment

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