Head pose estimation by regression algorithm

Abate, Andrea F.; Barra, Paola; Pero, Chiara; Tucci, Maurizio

doi:10.1016/j.patrec.2020.10.003

Cited by 22 publications

(11 citation statements)

References 15 publications

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“…Our method can infer the head posture from only an image without additional factors such as a depth map or facial markers. Initially, MTCNN [37] was used to detect the target, which was then divided into three levels. A residual attention block and SE block were used for feature extraction.…”

Section: Discussionmentioning

confidence: 99%

“…Wu used hog and pyramid settings to describe local gradient features and global shape features of the image of the face to facilitate head pose estimation in the local occlusion state [36]. Abate [37] proposed the Web-shaped Model algorithm to encode the posture of the face, and then regression for further face posture prediction. This method improves the sensitivity of head posture estimation and prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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MR-CapsNet: A Deep Learning Algorithm for Image-Based Head Pose Estimation on CapsNet

Fang

Liu

Xie³

et al. 2021

IEEE Access

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Head pose estimation based on a single image is a challenging endeavor because of the complex background conditions and characteristics of the human face. In this report, we propose a Multi stage Regression-Capsule Network (MR-CapsNet) to predict head posture based on a single image input. In the study, we used the residual attention block and squeeze-and-excitation block to extract features in three levels. CapsNet overcomes the shortcomings of the traditional convolutional neural network and implements module aggregation to describe the spatial relationship of features after aggregation, in addition to realizing a compact and robust model using a multi-stage regression scheme. We tested our method on the AFLW2000 and BIWI datasets obtaining mean absolute errors of 4.26% and 3.95%, respectively. In addition, we discuss the accuracy of our method in the case of eye or mouth occlusion. The results of comprehensive experiments reveal that our method can accurately predict head posture.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MR-CapsNet: A Deep Learning Algorithm for Image-Based Head Pose Estimation on CapsNet

Fang

Liu

Xie³

et al. 2021

IEEE Access

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“…This method does not make use of neural networks. The papers [2,3] obtain a face pose coding building a Web-Shaped Model through the reference points of the face. In hGLLiM [10], they experiment different classifiers and regression methods, proposing to use a mixture of linear regressions that learns to map high-dimensional feature vectors (extracted from the face bounding boxes) on the head pose angles and the bounding box displacements, so that they are predicted in robust way in the presence of unobservable phenomena.…”

Section: D Image Methodsmentioning

confidence: 99%

Partitioned iterated function systems by regression models for head pose estimation

Abate

Barra

Pero

et al. 2021

Machine Vision and Applications

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Head pose estimation represents an important computer vision technique in different contexts where image acquisition cannot be controlled by an operator, making face recognition of unknown subjects more accurate and efficient. In this work, starting from partitioned iterated function systems to identify the pose, different regression models are adopted to predict the angular value errors (yaw, pitch and roll axes, respectively). This method combines the fractal image compression characteristics, such as self-similar structures in order to identify similar head rotation, with regression analysis prediction. The experimental evaluation is performed on widely used benchmark datasets, i.e., Biwi and AFLW2000, and the results are compared with many existing state-of-the-art methods, demonstrating the robustness of the proposed fusion approach and excellent performance.

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“…Many methods have incorporated head pose estimation without any training of neural networks. In [24], the web-based model is combined with the regression model to estimate head pose.…”

Section: A Regression-based Methodsmentioning

confidence: 99%

Attention Span Prediction Using Head-Pose Estimation With Deep Neural Networks

Singh

Mohadikar

Gite³

et al. 2021

IEEE Access

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Automated human pose estimation is evolving as an exciting research area in human activity detection. It includes sophisticated applications such as malpractice detection in the examination, distracted driving, gesture detection, etc., and requires robust and reliable pose estimation techniques. These applications help to map the attention of the user with head pose estimation (HPE) metrics supported by emotion and gaze analysis. This paper solves the problem of attention score estimation with HPE. The proposed method ensures ease of implementation while addressing head pose estimation using 68 facial features. Further, to attain reliability and precision, head pose estimation has been implemented as a regression task. The coordinate pair angle method (CPAM) with deep neural network (DNN) regression and elastic net regression is carried out. The use of DNN ensures precision on low lighting, distorted or occluded images. CPAM methodology leverages facial landmark detection and angular difference to estimate head pose. Experimentation results showed that the proposed model could handle large datasets, real-time data processing, significant pose variations, partial occlusions, and diverse facial expressions with a mean absolute error (MAE) of 3° and less. The proposed system was evaluated on three standard databases: the 300W across large poses (300W-LP) dataset, annotated facial landmarks in the wild (AFLW2000) dataset, and the national institute of mental health child emotional faces picture set (NIMH-ChEFS) dataset. The results achieved are on par with recent state-of-the-art methodologies such as anisotropic angle distribution learning (AADL), joint head pose estimation and face alignment algorithm (JFA), rotation axis focused attention network (RAFA-Net), and propose an MAE ranging up to 6°. The paper could achieve remarkable results for attention span prediction using head pose estimation and for many possible future applications.

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Head pose estimation by regression algorithm

Cited by 22 publications

References 15 publications

MR-CapsNet: A Deep Learning Algorithm for Image-Based Head Pose Estimation on CapsNet

MR-CapsNet: A Deep Learning Algorithm for Image-Based Head Pose Estimation on CapsNet

Partitioned iterated function systems by regression models for head pose estimation

Attention Span Prediction Using Head-Pose Estimation With Deep Neural Networks

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