Joint head pose and facial landmark regression from depth images

Wang, Jie; Zhang, Jie; Luo, Changwei; Chen, Falai

doi:10.1007/s41095-017-0082-8

Cited by 17 publications

(6 citation statements)

References 38 publications

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“…In separate work, [34] used a similar approach by proposing an application of cascaded random forest on nine sub-space classifications of the head pose with each specific space trained by a global shape constraint. This classificationbased method efficiently handled the problem of significant pose variations.…”

Section: B Facial Feature Recommendationsmentioning

confidence: 99%

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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: B Facial Feature Recommendationsmentioning

confidence: 99%

“…With the strong computational ability and robust design feature, the deep learning model keeps the features with maximum weight and contribution and discards the rest. It gives results based on a series of non-linear operations [34].…”

Section: B Datasetsmentioning

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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“…For example, the head pose of a user may indicate that the user is not looking at the screen. Research in this domain has shown that head pose and facial landmarks, such as eye corners, nose tip, mouth, and chin, can accurately be detected and that this detection is important for face detection and recognizing facial expressions [37]. Facial expressions of users might reveal their feelings about the content or their experience of using the service.…”

Section: Related Workmentioning

confidence: 99%

Evaluating facial recognition services as interaction technique for recommender systems

Pessemier

Coppens

Martens

2020

Multimed Tools Appl

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Recommender systems are tools and techniques to assist users in the content selection process thereby coping with the problem of information overload. For recommender systems, user authentication and feedback gathering are of crucial importance. However, the typical user authentication with username / password and feedback method with a star rating system are not user friendly and often bypassed. This article proposes an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics such as emotions. We studied the use case of video watching. Photos made with the front-facing camera of a tablet, smartphone, or smart TV are used as input of a facial recognition service. The persons in front of the screen can be identified. During video watching, implicit feedback for the video content is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An evaluation with a test panel showed that the recognized emotions are correlated with the user's star ratings and that happiness can be most accurately detected. So as the main contribution, this article indicates that emotion recognition might be used as an alternative feedback mechanism for recommender systems.

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“…In recent years, research related to "humans" in the computer vision community has become increasingly active because of the high demand for real-life applications. There has been much good research in the fields of human pose estimation [1,2,6,14,20,26,40], pedestrian detection [25,41,42], portrait segmentation [35,36,37], and face recognition [18,23,24,27,39,43,44], much of which has already produced practical value in real life. This paper focuses on multi-person pose estimation and human instance segmentation, and proposes a pose-based human instance segmentation framework.…”

Section: Introductionmentioning

confidence: 99%

Pose2Seg: Detection Free Human Instance Segmentation

Zhang

Dong

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

169

119

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The standard approach to image instance segmentation is to perform the object detection first, and then segment the object from the detection bounding-box. More recently, deep learning methods like Mask R-CNN [14] perform them jointly. However, little research takes into account the uniqueness of the "human" category, which can be well defined by the pose skeleton. Moreover, the human pose skeleton can be used to better distinguish instances with heavy occlusion than using bounding-boxes. In this paper, we present a brand new pose-based instance segmentation framework 1 for humans which separates instances based on human pose, rather than proposal region detection. We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detectionbased approach on the human instance segmentation problem, and can moreover better handle occlusion. Furthermore, there are few public datasets containing many heavily occluded humans along with comprehensive annotations, which makes this a challenging problem seldom noticed by researchers. Therefore, in this paper we introduce a new benchmark "Occluded Human (OCHuman)" 2 , which focuses on occluded humans with comprehensive annotations including bounding-box, human pose and instance masks. This dataset contains 8110 detailed annotated human instances within 4731 images. With an average 0.67 Max-IoU for each person, OCHuman is the most complex and challenging dataset related to human instance segmentation. Through this dataset, we want to emphasize occlusion as a challenging problem for researchers to study.

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Joint head pose and facial landmark regression from depth images

Cited by 17 publications

References 38 publications

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

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

Evaluating facial recognition services as interaction technique for recommender systems

Pose2Seg: Detection Free Human Instance Segmentation

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