Accurate Head Pose Estimation Research Articles

Self-Attention Mechanism-Based Head Pose Estimation Network with Fusion of Point Cloud and Image Features.

Head pose estimation serves various applications, such as gaze estimation, fatigue-driven detection, and virtual reality. Nonetheless, achieving precise and efficient predictions remains challenging owing to the reliance on singular data sources. Therefore, this study introduces a technique involving multimodal feature fusion to elevate head pose estimation accuracy. The proposed method amalgamates data derived from diverse sources, including RGB and depth images, to construct a comprehensive three-dimensional representation of the head, commonly referred to as a point cloud. The noteworthy innovations of this method encompass a residual multilayer perceptron structure within PointNet, designed to tackle gradient-related challenges, along with spatial self-attention mechanisms aimed at noise reduction. The enhanced PointNet and ResNet networks are utilized to extract features from both point clouds and images. These extracted features undergo fusion. Furthermore, the incorporation of a scoring module strengthens robustness, particularly in scenarios involving facial occlusion. This is achieved by preserving features from the highest-scoring point cloud. Additionally, a prediction module is employed, combining classification and regression methodologies to accurately estimate head poses. The proposed method improves the accuracy and robustness of head pose estimation, especially in cases involving facial obstructions. These advancements are substantiated by experiments conducted using the BIWI dataset, demonstrating the superiority of this method over existing techniques.

Accurate Head Pose Estimation Using Image Rectification and a Lightweight Convolutional Neural Network

Head pose estimation is an important step for many human-computer interaction applications such as face detection, facial recognition, and facial expression classification. Accurate head pose estimation benefits these applications that require face images as the input. Most head pose estimation methods suffer from perspective distortion because the users do not always align their face perfectly with the camera. This paper presents a new approach that uses image rectification to reduce the negative effect of perspective distortion and a lightweight convolutional neural network to obtain highly accurate head pose estimations. The proposed method calculates the angle between the optical axis of the camera and the projection vector of the center of the face. The face image is rectified using this estimated angle through perspective transformation. A lightweight network that is only 0.88 MB in size is designed to take the rectified face image as the input to perform head pose estimation. The output of the network, the head pose estimation of the rectified face image, is transformed back to the camera coordinate system as the final head pose estimation. Experiments on public benchmark datasets show that the proposed image rectification method and the newly designed lightweight network improve the accuracy of head pose estimation remarkably. Compared with state-of-the-art methods, our approach achieves both higher accuracy and faster processing speed.

Head Pose Estimation in Complex Environment Based on Four-Branch Feature Selective Extraction and Regional Information Exchange Fusion Network

Under the severe situation of the COVID-19 pandemic, masks cover most of the effective facial features of users, and their head pose changes significantly in a complex environment, which makes the accuracy of head pose estimation in some systems such as safe driving systems and attention detection systems impossible to guarantee. To this end, we propose a powerful four-branch feature selective extraction network (FSEN) structure, in which three branches are used to extract three independent discriminative features of pose angles, and one branch is used to extract composite features corresponding to multiple pose angles. By reducing the dimension of high-dimensional features, our method significantly reduces the amount of computation while improving the estimation accuracy. Our convolution method is an improved spatial channel dynamic convolution (SCDC) that initially enhances the extracted features. Additionally, we embed a regional information exchange network (RIEN) after each convolutional layer in each branch to fully mine the potential semantic correlation between regions from multiple perspectives and learn and fuse this correlation to further enhance feature expression. Finally, we fuse the independent discriminative features of each pose angle and composite features from the three directions of channel, space, and pixel to obtain perfect feature expression for each pose angle, and then obtain the head pose angle. We conducted extensive experiments on the controlled environment datasets and a self-built real complex environment dataset (RCE) and the results showed that our method outperforms state-of-the-art single-modality methods and performs on par with multimodality-based methods. This shows that our network meets the requirements of accurate head-pose estimation in real complex environments such as complex illumination and partial occlusion.

Head pose estimation using deep neural networks and 3D point clouds

In this paper, we propose head pose estimation using deep neural networks and 3D point cloud. Unlike existing methods that either take 2D RGB image or 2D depth image as input, we adopt 3D point cloud data generated from depth to estimate 3D head poses. To further improve robustness and accuracy of head pose estimation, we classify 3D angles of head poses into 36 classes with 5 ∘ interval and predict the probability of each angle in a class based on multi-layer perceptron (MLP). While traditional iterative methods for head model construction require high computation and memory costs, the proposed method is lightweight and computationally efficient by utilizing a sampled 3D point cloud as input combined with a graph convolutional neural network (GCNN). Experimental results on Biwi Kinect Head Pose dataset show that the proposed method achieves outstanding performance in head pose estimation and outperforms state-of-the-art ones in terms of accuracy.

Learning 3D Head Pose From Synthetic Data: A Semi-Supervised Approach

Accurate head pose estimation from 2D image data is an essential component of applications such as driver monitoring systems, virtual reality technology, and human-computer interaction. It enables a better determination of user engagement and attentiveness. The most accurate head pose estimators are based on Deep Neural Networks that are trained with the supervised approach and rely primarily on the accuracy of training data. The acquisition of real head pose data with a wide variation of yaw, pitch and roll is a challenging task. Publicly available head pose datasets have limitations with respect to size, resolution, annotation accuracy and diversity. In this work, a methodology is proposed to generate pixel-perfect synthetic 2D headshot images rendered from high-quality 3D synthetic facial models with accurate head pose annotations. A diverse range of variations in age, race, and gender are also provided. The resulting dataset includes more than 300k pairs of RGB images with corresponding head pose annotations. A wide range of variations in pose, illumination and background are included. The dataset is evaluated by training a state-of-the-art head pose estimation model and testing against the popular evaluation-dataset Biwi. The results show that training with purely synthetic data generated using the proposed methodology achieves close to state-of-the-art results on head pose estimation which are originally trained on real human facial datasets. As there is a domain gap between the synthetic images and real-world images in the feature space, initial experimental results fall short of the current state-of-the-art. To reduce the domain gap, a semi-supervised visual domain adaptation approach is proposed, which simultaneously trains with the labelled synthetic data and the unlabeled real data. When domain adaptation is applied, a significant improvement in model performance is achieved. Additionally, by applying a data fusion-based transfer learning approach, better results are achieved than previously published work on this topic.

Real-Time Head Pose Estimation and Face Modeling From a Depth Image

We address the issues of 3-D head pose estimation and face modeling from a depth image. Given a depth image, random forests are effective for estimating the location and orientation of a person's head. However, the accuracy of the estimation is not high enough. We propose using corrected regression votes. The corrected votes are obtained by considering the cooperation of all trees, leading to significant improvement of head pose estimation accuracy. Based on the head pose estimator, we present a face modeling system. In our system, the face model is generated by aligning a deformable face model to a depth image using an iterative closest point (ICP) algorithm. The novelty of our approach is that an optimal weight for each vertex is incorporated into the ICP algorithm with point to plane constraints. Experiments show that our system can automatically estimate the head pose and generate a realistic face model from a single depth image. We also provide a detailed evaluation that shows the benefits of our approach.

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.

Learning toward practical head pose estimation

Head pose is useful information for many face-related tasks, such as face recognition, behavior analysis, human–computer interfaces, etc. Existing head pose estimation methods usually assume that the face images have been well aligned or that sufficient and precise training data are available. In practical applications, however, these assumptions are very likely to be invalid. This paper first investigates the impact of the failure of these assumptions, i.e., misalignment of face images, uncertainty and undersampling of training data, on head pose estimation accuracy of state-of-the-art methods. A learning-based approach is then designed to enhance the robustness of head pose estimation to these factors. To cope with misalignment, instead of using hand-crafted features, it seeks suitable features by learning from a set of training data with a deep convolutional neural network (DCNN), such that the training data can be best classified into the correct head pose categories. To handle uncertainty and undersampling, it employs multivariate labeling distributions (MLDs) with dense sampling intervals to represent the head pose attributes of face images. The correlation between the features and the dense MLD representations of face images is approximated by a maximum entropy model, whose parameters are optimized on the given training data. To estimate the head pose of a face image, its MLD representation is first computed according to the model based on the features extracted from the image by the trained DCNN, and its head pose is then assumed to be the one corresponding to the peak in its MLD. Evaluation experiments on the Pointing’04, FacePix, Multi-PIE, and CASIA-PEAL databases prove the effectiveness and efficiency of the proposed method.

Model-free non-rigid head pose tracking by joint shape and pose estimation

Head pose estimation under non-rigid face movement is particularly useful in applications relating to eye-gaze tracking in less constrained scenarios, where the user is allowed to move naturally during tracking. Existing vision-based head pose estimation methods often require accurate initialisation and tracking of specific facial landmarks, while methods that handle non-rigid face deformations typically necessitate a preliminary training phase prior to head pose estimation. In this paper, we propose a method to estimate the head pose in real-time from the trajectories of a set of feature points spread randomly over the face region, without requiring a training phase or model-fitting of specific facial features. Conversely, our method exploits the 3-dimensional shape of the surface of interest, recovered via shape and motion factorisation, in combination with Kalman and particle filtering to determine the contribution of each feature point to the estimation of head pose based on a variance measure. Quantitative and qualitative results reveal the capability of our method in handling non-rigid face movement without deterioration of the head pose estimation accuracy.

Training‐based head pose estimation under monocular vision

Although many 3D head pose estimation methods based on monocular vision can achieve an accuracy of 5°, how to reduce the number of required training samples and how to not to use any hardware parameters as input features are still among the biggest challenges in the field of head pose estimation. To aim at these challenges, the authors propose an accurate head pose estimation method which can act as an extension to facial key point detection systems. The basic idea is to use the normalised distance between key points as input features, and to use ℓ1‐minimisation to select a set of sparse training samples which can reflect the mapping relationship between the feature vector space and the head pose space. The linear combination of the head poses corresponding to these samples represents the head pose of the test sample. The experiment results show that the authors’ method can achieve an accuracy of 2.6° without any extra hardware parameters or information of the subject. In addition, in the case of large head movement and varying illumination, the authors’ method is still able to estimate the head pose.

Head Detection by the Adaboost Algorithm for Head Pose Estimation

For human head pose analysis based on videos, pose is usually estimated on the head patch provided by a tracking module. However, head tracking is very sensitive to the large changes of pose. Therefore, this work locates the head patch in the videos by head detection. Firstly, we use the Adaboost algorithm to detect the human head in the video. Secondly, we present a dimensionality reduction method to process the head patch. Finally, we use the nearest neighbor method to estimate the head pose. The experiment results show: accurate head detecting helps to estimate the head pose. This method can be used for complex conditions of accurate head 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.

Iterative three-dimensional head pose estimation using a face normal vector

The performance of face recognition systems has much been burdened by head pose variation. To solve this problem, 3-D face recognition systems that make use of multiple views and depth information have been suggested. However, without an accurate head pose estimation, the performance improvement of 3-D face recognition systems under pose variations remains limited. Previous research on 3-D head pose estimation has been conducted in 3-D space, where the estimation complexity is high. Also it is difficult to incorporate those salient 2-D face features for effective estimation. We propose a novel iterative 3-D head pose estimation method incorporating both 2-D and 3-D face information. To verify the effectiveness, we apply the proposed method to 3-D face modeling and recognition systems with adaptation to various 3-D face data acquisition devices. Our experimental results show that the proposed method can be very effective in terms of modeling and recognition applications, particularly on combining different kinds of acquisition devices, which use different coordinates of origin and scale.