3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

Khan, Khalil; Ali, Jehad; Ahmad, Kashif; Gul, Asma; Sarwar, Ghulam; Khan, Sahib; Ta, Qui Thanh Hoai; Chung, Tae-Sun; Attique, Muhammad

doi:10.32604/cmc.2020.013590

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…More modern works address segmentation by means of Deep Neural Networks, that typically allow to consider a larger number of segmentation classes, and discrete poses (e.g. 13 poses [29,31] or 93 poses [30,32,80]).…”

Section: Segmentation Based Methodsmentioning

confidence: 99%

Deep Learning for Head Pose Estimation: A Survey

Asperti

Filippini

2023

SN COMPUT. SCI.

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Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the field by proposing a classification of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the different ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic.

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Section: Segmentation Based Methodsmentioning

confidence: 99%

Deep Learning for Head Pose Estimation: A Survey

Asperti

Filippini

2023

SN COMPUT. SCI.

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“…In [26], the face is partitioned into seven different facial features, and accurate head pose estimation is done using DCNN, as shown in figure 2. Facial landmarks have also been evaluated through a heatmap generator in a feedforward neural network [27].…”

Section: B Facial Feature Recommendationsmentioning

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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“…Compared with the previous method, this method can obtain very high-precision output, and a new method of marking cracks is proposed, which is beneficial to the measurement of crack length and width. Hoskere et al [27] proposed a structural damage detection method based on multiscale pixel-level deep convolutional neural network [28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

Meng

2021

Scientific Programming

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Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.

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3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

Cited by 10 publications

References 21 publications

Deep Learning for Head Pose Estimation: A Survey

Deep Learning for Head Pose Estimation: A Survey

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

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

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