Transfer useful knowledge for headpose estimation from low resolution images

Li, Ping-An; Li, Yufeng; Tan, Lixin

doi:10.1007/s11042-016-3297-2

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…The proposed method uses dense sampling intervals with multivariate labeling distributions (MLDs) to show input face image head pose angles. Unstructured behavior of the dynamic environment makes focus-of-attention(FOA) challenging; it gives rise to the possibility of poor quality, occlusion, low-resolution image, disturbance, and non-linear relationship between head pose angle and ground truth value [16]. This research gap encouraged us to incorporate HPE based attention mapping.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Transfer learning, which studies how to transfer the knowledge learned from the source domain to the target domain, can solve new problems faster and better with a small amount of data and low cost [25]. Transfer learning is an effective method when lacking data, and it has achieved many results in the research of face pose estimation [26]- [29]. These successes motivate us to use transfer learning to solve the problem in this paper.…”

Section: Introductionmentioning

confidence: 99%

Efficient Transfer Learning Combined Skip-Connected Structure for Masked Face Poses Classification

2020

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Aiming at the new requirements of masked face poses classification during the epidemic outbreak, this paper proposes an efficient transfer learning approach combined skip-connected structure to improve the accuracy of masked face poses classification in the absence of masked face poses data. We have worked on the following two aspects: 1) According to the features transition of the convolutional neural networks, we propose an efficient transfer learning approach and opt for a more appropriate source domain to solve the problem that the specificity of features in the pre-trained deep networks will damage the performance when transferring to the target domain. First, a semisynthetic masked face poses dataset is constructed to replace ImageNet as the source domain, which can reduce the span of transfer and improve the pertinence of transfer learning. Second, the shallow networks which contain the general features are frozen while the deep networks which contain the specific features are retrained and the entire networks are finetuned afterwards. It optimizes the specific features in the source domain when transferring, and promoted transfer learning more effectively; 2) To further improve the overall accuracy by improving the accuracy of masked face pose classes with subtle differences, a skip-connected structure is proposed to fuse general features containing rich detailed information in the shallow networks into the classifier. Experiments on AlexNet and VGG16 show that the proposed method has certain advantages, and the overall accuracy can reach 96.43% and 99.29% at the final respectively. INDEX TERMS Masked face pose classification, transfer learning, skip-connected structure, detailed feature and semantic feature, deep learning.

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Transfer useful knowledge for headpose estimation from low resolution images

Cited by 2 publications

References 27 publications

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

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

Efficient Transfer Learning Combined Skip-Connected Structure for Masked Face Poses Classification

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