Problem Of Head Pose Estimation Research Articles

HeadDiff: Exploring Rotation Uncertainty With Diffusion Models for Head Pose Estimation.

In this paper, we propose a probabilistic regression diffusion model for head pose estimation, dubbed HeadDiff, which typically addresses the rotation uncertainty, especially when faces are captured in wild conditions. Unlike conventional image-to-pose methods which cannot explicitly establish the rotational manifold of head poses, our HeadDiff aims to ensure the pose rotation via the diffusion process and in parallel, refine the mapping process iteratively. Specifically, we initially formulate the head pose estimation problem as a reverse diffusion process, defining a paradigm for progressive denoising on the manifold, which explores the uncertainty by decomposing the large gap into intermediate steps. Moreover, our HeadDiff is equipped with an isotropic Gaussian distribution by encoding the incoherence information in our rotation representation. Finally, we learn the facial relationship of nearest neighbors with a cycle-consistent constraint for robust pose estimation versus diverse shape variations. Experimental results on multiple datasets demonstrate that our proposed method outperforms existing state-of-the-art techniques without auxiliary data.

2D Image head pose estimation via latent space regression under occlusion settings

Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems still underperform in the presence of occlusions and are unreliable for many task applications in such scenarios. This work proposes a novel deep learning approach for the problem of head pose estimation under occlusions. The strategy is based on latent space regression as a fundamental key to better structure the problem for occluded scenarios. Our model surpasses several state-of-the-art methodologies for occluded HPE, and achieves similar accuracy for non-occluded scenarios. We demonstrate the usefulness of the proposed approach with: (i) two synthetically occluded versions of the BIWI and AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii) a real-life application to human-robot interaction scenarios where face occlusions often occur. Specifically, the autonomous feeding from a robotic arm.

Human head pose estimation based on HF method

This Paper addresses the problem of head pose estimation . Driving assistance technology utilizes head pose estimation as an indicator for visual focus and mental attention of the driver. Head pose estimation detects head orientation with respect to the camera. Model based and appearance-based methods are the two approaches in head pose estimation. The first approach uses the facial features to create a face geometrical models whereas the second method only takes into consideration the entire face image. The proposed appearance-based method work is performed using Hough transform and random forest to classify ninety-three classes of Hough values in order to find the exact head pose. The performance of the proposed work is evaluated based on accuracy and the time taken to detect the head pose. The paper outperforms many other previous works.

QuatNet: Quaternion-Based Head Pose Estimation With Multiregression Loss

Head pose estimation has attracted immense research interest recently, as its inherent information significantly improves the performance of face-related applications such as face alignment and face recognition. In this paper, we conduct an in-depth study of head pose estimation and present a multiregression loss function, an L2 regression loss combined with an ordinal regression loss, to train a convolutional neural network (CNN) that is dedicated to estimating head poses from RGB images without depth information. The ordinal regression loss is utilized to address the nonstationary property observed as the facial features change with respect to different head pose angles and learn robust features. The L2 regression loss leverages these features to provide precise angle predictions for input images. To avoid the ambiguity problem in the commonly used Euler angle representation, we further formulate the head pose estimation problem in quaternions. Our quaternion-based multiregression loss method achieves state-of-the-art performance on the AFLW2000, AFLW test set, and AFW datasets and is closing the gap with methods that utilize depth information on the BIWI dataset.

Head Pose Estimation in the Wild Assisted by Facial Landmarks Based on Convolutional Neural Networks

Convolutional neural networks (CNNs) exhibit excellent performance on the head pose estimation problem under controllable conditions, but their generalization ability in the wild needs to be improved. To address this issue, we propose an approach involving the introduction of facial landmark information into the task simplifier and landmark heatmap generator constructed before the feed-forward neural network, which can use this information to normalize the face shape into a canonical shape and generate a landmark heatmap based on the transformed facial landmarks to assist in feature extraction, for enhancing generalization ability in the wild. Our method was trained on 300W-LP and tested on AFLW2000-3D. The result shows that for the same feed-forward neural network when our method is used to introduce facial landmark information into a CNN, accuracy improves from 88.5% to 99.0% and mean average error decreases from 5.94° to 1.46° on AFLW2000-3D. Furthermore, we evaluate our method on several datasets used for pose estimation and compare the result with AFLW2000-3D, finding that the features extracted by a CNN could not reflect the head pose efficiently, which limits the performance of the CNN on the head pose estimation problem in wild. By introducing facial landmarks, the CNN could extract features that reflect head pose more efficiently, thereby significantly improving the accuracy of head pose estimation in the wild.

Non-Stationary Representation for Continuity Aware Head Pose Estimation Via Deep Neural Decision Trees

The problem of image-based head pose estimation attracts intensive attention due to a large number of applications such as face analysis and attention modeling. Existing methods often convert head pose estimation into the pose classification problem, but ignored the non-stationary appearance change brought about by the equally distributed bins. This paper targets the head pose estimation problem via deep neural decision trees, where the non-linear property of the representative appearance is learned together with the bin classification probability. First, we use Convolutional Neural Network (CNN) to get pose related features. Second, we apply the Fully Connected (FC) layer on the learned features to extract branch weight for each Euler angle and the representative values for each bin. Third, we employ neural decision tree on the branch weight to get bin classification probability. To explicitly characterize the relationship between the adjacent pose intervals, we embed continuity of the head angles into the tree architecture by constructing the bridge-tree. The final estimation is obtained via a weighted sum between the estimated bin probability and the representative bin values. We evaluate our methods on different public datasets including Pointing’04, Chinese Academy of Sciences-Pose, Expression, Accessory, and Lighting (CAS-PEAL) and Biwi Kinect Head Pose and find that, the proposed method outperforms as compared to state-of-the-art. Besides, we leverage the template marching based alignment for data preprocessing and demonstrate its superiority over traditional alignment methods on the task of head pose estimation.

Head pose estimation based on face symmetry analysis

This paper addresses the problem of head pose estimation in order to infer non-intrusive feedback from users about gaze attention. The proposed approach exploits the bilateral symmetry of the face. Size and orientation of the symmetrical area of the face is used to estimate roll and yaw poses by the mean of decision tree model. The approach does not need the location of interest points on face and presents robustness to partial occlusions. Tests were performed on different datasets (FacePix, CMU PIE, Boston University) and our approach coped with variability in illumination and expressions. Results demonstrate that the changes in the size of the regions that contain a bilateral symmetry provide accurate pose estimation.

An effective head pose estimation approach using Lie Algebrized Gaussians based face representation

The accuracy of head pose estimation is significant for many computer vision applications such as face recognition, driver attention detection and human-computer interaction. Most appearance-based head pose estimation works typically extract the low-dimensional 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 appearance-based 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 multi-resolution 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 within-class covariance normalization (WCCN) based Support Vector Machine (SVM) classifier is employed to achieve robust performance as WCCN could reduce the within-class variabilities of the same pose. Extensive experimental analysis and comparison with both traditional and state-of-the-art algorithms on two challenging benchmarks demonstrate the effectiveness of our approach.

A novel feature descriptor based on biologically inspired feature for head pose estimation

This paper proposes a novel method to improve the accuracy of head pose estimation. Since biologically inspired features (BIF) have been demonstrated to be both effective and efficient for many visual tasks, we argue that BIF can be applied to the problem of head pose estimation. By combining the BIF with the well-known local binary pattern (LBP) feature, we propose a novel feature descriptor named “local biologically inspired features” (LBIF). Considering that LBIF is extrinsically very high dimensional, ensemble-based supervised methods are applied to reduce the dimension while at the same time improving its discriminative ability. Results obtained from the evaluation on two different databases show that the proposed LBIF feature achieves significant improvements over the state-of-the-art methods of head pose estimation.

Multi-camera head pose estimation

Estimating people’s head pose is an important problem, for which many solutions have been proposed. Most existing solutions are based on the use of a single camera and assume that the head is confined in a relatively small region of space. If we need to estimate unintrusively the head pose of persons in a large environment, however, we need to use several cameras to cover the monitored area. In this work, we propose a novel solution to the multi-camera head pose estimation problem that exploits the additional amount of information that provides multi-camera configurations. Our approach uses the probability estimates produced by multi-class support vector machines to calculate the probability distribution of the head pose. The distributions produced by the cameras are fused, resulting in a more precise estimate than the one provided individually. We report experimental results that confirm that the fused distribution provides higher accuracy than the individual classifiers and a high robustness against errors.

Boosting feature selection for Neural Network based regression

The head pose estimation problem is well known to be a challenging task in computer vision and is a useful tool for several applications involving human-computer interaction. This problem can be stated as a regression one where the input is an image and the output is pan and tilt angles. Finding the optimal regression is a hard problem because of the high dimensionality of the input (number of image pixels) and the large variety of morphologies and illumination. We propose a new method combining a boosting strategy for feature selection and a neural network for the regression. Potential features are a very large set of Haar-like wavelets which are well known to be adapted to face image processing. To achieve the feature selection, a new Fuzzy Functional Criterion (FFC) is introduced which is able to evaluate the link between a feature and the output without any estimation of the joint probability density function as in the Mutual Information. The boosting strategy uses this criterion at each step: features are evaluated by the FFC using weights on examples computed from the error produced by the neural network trained at the previous step. Tests are carried out on the commonly used Pointing 04 database and compared with three state-of-the-art methods. We also evaluate the accuracy of the estimation on FacePix, a database with a high angular resolution. Our method is compared positively to a Convolutional Neural Network, which is well known to incorporate feature extraction in its first layers.