Automatic head pose estimation with Synchronized sub manifold embedding and Random Regression Forests

Zhu, Yulian; Xue, Zhimei; Li, Chunyan

doi:10.14257/ijsip.2014.7.3.11

Cited by 7 publications

(2 citation statements)

References 28 publications

(27 reference statements)

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“…The main idea behind these methods is that, regardless of the dimensionality of the input features representing the mesh, there should be at most three degrees of freedom for head pose variation, thus defining a 3D manifold (Raytchev et al, 2004). However, in general, this manifold is embedded non-linearly in the ambient space defined by the features, which has led researchers to explore non-linear manifold learning methods such as Locally Linear Embedding (Fu and Huang, 2006), Isomap (Raytchev et al, 2004), Synchronized Submanifold Embedding (Zhu et al, 2014), Homeomorphic Manifold Analysis (Peng et al, 2014), Neighborhood Preserving Embedding or Locality Preserving Projection (BenAbdelkader, 2010) for head pose estimation from 2D images.…”

Section: Manifold-based Methodsmentioning

confidence: 99%

Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation

2019

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Head pose estimation is a challenging computer vision problem with important applications in different scenarios such as human-computer interaction or face recognition. In this paper, we present a 3D head pose estimation algorithm based on non-linear manifold learning. A key feature of the proposed approach is that it allows modeling the underlying 3D manifold that results from the combination of rotation angles. To do so, we use tensor decomposition to generate separate subspaces for each variation factor and show that each of them has a clear structure that can be modeled with cosine functions from a unique shared parameter per angle. Such representation provides a deep understanding of data behavior. We show that the proposed framework can be applied to a wide variety of input features and can be used for different purposes. Firstly, we test our system on a publicly available database, which consists of 2D images and we show that the cosine functions can be used to synthesize rotated versions from an object from which we see only a 2D image at a specific angle. Further, we perform 3D head pose estimation experiments using other two types of features: automatic landmarks and histogram-based 3D descriptors. We evaluate our approach on two publicly available databases, and demonstrate that angle estimations can be performed by optimizing the combination of these cosine functions to achieve state-of-the-art performance.

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Section: Manifold-based Methodsmentioning

confidence: 99%

Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation

2019

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“…These methods determine the gaze direction by obtaining mapping functions from the features of 2D eye images [ 10 ]. These methods are quite simple but are unsuitable for many real-world applications because they have difficulty finding head movements [ 11 ]. Model-based approaches use three-dimensional geometrical relationships among the eyes, infrared light sources, and camera positions.…”

Section: Related Workmentioning

confidence: 99%

A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications

Vankayalapati

Kuchibhotla

Chadalavada

et al. 2022

Sensors

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A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human–machine or human–robot interactions. For example, in high-accuracy advent applications such as Driver’s Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.

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Estimating Driver Attentiveness Through Head Pose Using Hybrid Geometric-Based Method

Vankayalapati¹,

Anne²,

Harsha³

2022

Smart Intelligent Computing and Applications, Volume 1

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Automatic head pose estimation with Synchronized sub manifold embedding and Random Regression Forests

Abstract: Abstract

Cited by 7 publications

References 28 publications

Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation

Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation

A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications

Estimating Driver Attentiveness Through Head Pose Using Hybrid Geometric-Based Method

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