The Experts below are selected from a list of 3435 Experts worldwide ranked by ideXlab platform
Georgios Evangelidis - One of the best experts on this subject based on the ideXlab platform.
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Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
IEEE Transactions on Image Processing, 2017Co-Authors: Vincent Drouard, Radu Horaud, Antoine Deleforge, Georgios EvangelidisAbstract:Head-Pose Estimation has many applications, such as social-event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-Pose Estimation is challenging because it must cope with changing illumination conditions, face orientation and appearance variabilities, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment problems. We proPose a mixture of linear regression method that learns how to map high-dimensional feature vectors (extracted from bounding-boxes of faces) onto both Head-Pose parameters and bounding-box shifts, such that at runtime they are simultaneously predicted. We describe in detail the mapping method that combines the merits of manifold learning and of mixture of linear regression. We validate our method with three publicly available datasets and we thoroughly benchmark four variants of the proPosed algorithm with several state-of-the-art Head-Pose Estimation methods.
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Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
IEEE Transactions on Image Processing, 2017Co-Authors: Vincent Drouard, Radu Horaud, Antoine Deleforge, Georgios EvangelidisAbstract:Head-Pose Estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-Pose Estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We proPose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of Head-Pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proPosed algorithm with several state-of-the-art Head-Pose Estimation methods.
Lijun Yin - One of the best experts on this subject based on the ideXlab platform.
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ICMI - Saliency-guided 3D Head Pose Estimation on 3D expression models
Proceedings of the 15th ACM on International conference on multimodal interaction - ICMI '13, 2013Co-Authors: Peng Liu, Michael Reale, Xing Zhang, Lijun YinAbstract:Head Pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human-computer interaction, multimedia, and vision systems. Robust Head Pose Estimation is a prerequisite for spontaneous facial biometrics-related applications. However, most previous Head Pose Estimation methods do not consider the facial expression and hence are more likely to be influenced by the facial expression. In this paper, we develop a saliency-guided 3D Head Pose Estimation on 3D expression models. We address the problem of Head Pose Estimation based on a generic model and saliency guided segmentation on a Laplacian fairing model. We proPose to perform mesh Laplacian fairing to remove noise and outliers on the 3D facial model. The salient regions are detected and segmented from the model. The salient region Iterative Closest Point (ICP) then register the test face model with the generic Head model. The algorithms for Pose Estimation are evaluated through both static and dynamic 3D facial databases. Overall, the extensive results demonstrate the effectiveness and accuracy of our approach.
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3d Head Pose Estimation based on scene flow and generic Head model
International Conference on Multimedia and Expo, 2012Co-Authors: Peng Liu, Michael Reale, Lijun YinAbstract:Head Pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human computer interaction, multimedia and vision systems. In this paper, we present a novel Head Pose Estimation system by performing Head region detection using the Kinect [2], followed by face detection, feature tracking, and finally Head Pose Estimation using an active camera. Ten feature points on the face are defined and tracked by an Active Appearance Model (AAM). We proPose to use the scene flow approach to estimate the Head Pose from 2D video sequences. This Estimation is based upon a generic 3D Head model through the prior knowledge of the Head shape and the geometric relationship between the 2D images and a 3D generic model. We have tested our Head Pose Estimation algorithm with various cameras at various distances in real time. The experiments demonstrate the feasibility and advantages of our system.
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ICME - 3D Head Pose Estimation Based on Scene Flow and Generic Head Model
2012 IEEE International Conference on Multimedia and Expo, 2012Co-Authors: Peng Liu, Michael Reale, Lijun YinAbstract:Head Pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human computer interaction, multimedia and vision systems. In this paper, we present a novel Head Pose Estimation system by performing Head region detection using the Kinect [2], followed by face detection, feature tracking, and finally Head Pose Estimation using an active camera. Ten feature points on the face are defined and tracked by an Active Appearance Model (AAM). We proPose to use the scene flow approach to estimate the Head Pose from 2D video sequences. This Estimation is based upon a generic 3D Head model through the prior knowledge of the Head shape and the geometric relationship between the 2D images and a 3D generic model. We have tested our Head Pose Estimation algorithm with various cameras at various distances in real time. The experiments demonstrate the feasibility and advantages of our system.
Vincent Drouard - One of the best experts on this subject based on the ideXlab platform.
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Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
IEEE Transactions on Image Processing, 2017Co-Authors: Vincent Drouard, Radu Horaud, Antoine Deleforge, Georgios EvangelidisAbstract:Head-Pose Estimation has many applications, such as social-event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-Pose Estimation is challenging because it must cope with changing illumination conditions, face orientation and appearance variabilities, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment problems. We proPose a mixture of linear regression method that learns how to map high-dimensional feature vectors (extracted from bounding-boxes of faces) onto both Head-Pose parameters and bounding-box shifts, such that at runtime they are simultaneously predicted. We describe in detail the mapping method that combines the merits of manifold learning and of mixture of linear regression. We validate our method with three publicly available datasets and we thoroughly benchmark four variants of the proPosed algorithm with several state-of-the-art Head-Pose Estimation methods.
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Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
IEEE Transactions on Image Processing, 2017Co-Authors: Vincent Drouard, Radu Horaud, Antoine Deleforge, Georgios EvangelidisAbstract:Head-Pose Estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-Pose Estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We proPose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of Head-Pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proPosed algorithm with several state-of-the-art Head-Pose Estimation methods.
Peng Liu - One of the best experts on this subject based on the ideXlab platform.
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ICMI - Saliency-guided 3D Head Pose Estimation on 3D expression models
Proceedings of the 15th ACM on International conference on multimodal interaction - ICMI '13, 2013Co-Authors: Peng Liu, Michael Reale, Xing Zhang, Lijun YinAbstract:Head Pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human-computer interaction, multimedia, and vision systems. Robust Head Pose Estimation is a prerequisite for spontaneous facial biometrics-related applications. However, most previous Head Pose Estimation methods do not consider the facial expression and hence are more likely to be influenced by the facial expression. In this paper, we develop a saliency-guided 3D Head Pose Estimation on 3D expression models. We address the problem of Head Pose Estimation based on a generic model and saliency guided segmentation on a Laplacian fairing model. We proPose to perform mesh Laplacian fairing to remove noise and outliers on the 3D facial model. The salient regions are detected and segmented from the model. The salient region Iterative Closest Point (ICP) then register the test face model with the generic Head model. The algorithms for Pose Estimation are evaluated through both static and dynamic 3D facial databases. Overall, the extensive results demonstrate the effectiveness and accuracy of our approach.
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3d Head Pose Estimation based on scene flow and generic Head model
International Conference on Multimedia and Expo, 2012Co-Authors: Peng Liu, Michael Reale, Lijun YinAbstract:Head Pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human computer interaction, multimedia and vision systems. In this paper, we present a novel Head Pose Estimation system by performing Head region detection using the Kinect [2], followed by face detection, feature tracking, and finally Head Pose Estimation using an active camera. Ten feature points on the face are defined and tracked by an Active Appearance Model (AAM). We proPose to use the scene flow approach to estimate the Head Pose from 2D video sequences. This Estimation is based upon a generic 3D Head model through the prior knowledge of the Head shape and the geometric relationship between the 2D images and a 3D generic model. We have tested our Head Pose Estimation algorithm with various cameras at various distances in real time. The experiments demonstrate the feasibility and advantages of our system.
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ICME - 3D Head Pose Estimation Based on Scene Flow and Generic Head Model
2012 IEEE International Conference on Multimedia and Expo, 2012Co-Authors: Peng Liu, Michael Reale, Lijun YinAbstract:Head Pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human computer interaction, multimedia and vision systems. In this paper, we present a novel Head Pose Estimation system by performing Head region detection using the Kinect [2], followed by face detection, feature tracking, and finally Head Pose Estimation using an active camera. Ten feature points on the face are defined and tracked by an Active Appearance Model (AAM). We proPose to use the scene flow approach to estimate the Head Pose from 2D video sequences. This Estimation is based upon a generic 3D Head model through the prior knowledge of the Head shape and the geometric relationship between the 2D images and a 3D generic model. We have tested our Head Pose Estimation algorithm with various cameras at various distances in real time. The experiments demonstrate the feasibility and advantages of our system.
Katsuyuki Fujimura - One of the best experts on this subject based on the ideXlab platform.
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Head Pose Estimation for Driver Monitoring
Proc. IEEE Intelligent Vehicles Symposium, 2004Co-Authors: Youding Zhu, Katsuyuki FujimuraAbstract:Head Pose Estimation is important for driver attention monitoring as well as for various human computer interaction tasks. In this paper, an adaptive Head Pose Estimation method is proPosed to overcome difficulties of existing approaches. The proPosed method is based on the analysis of two approaches for Head Pose Estimation from an image sequence, that is, principal component analysis (PCA) and 3D motion Estimation. The algorithm performs accurate Pose Estimation by learning the subject appearance on-line. Depth information is used effectively in the algorithm to segment the Head region even in a cluttered scene and to perform 3D Head motion Estimation based on optical flow constraints.
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Illumination invariant Head Pose Estimation using single camera
IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), 1Co-Authors: H. Nanda, Katsuyuki FujimuraAbstract:A learning based approach is presented for a single camera based robust Head Pose Estimation in highly cluttered and complex real world environments. The method makes effective use of a recently introduced real-time 3D depth sensing technology, resulting in illumination-invariant Head Pose Estimation. The main target application of our work is to classify the focus of attention of an automobile driver as straight-aHead, towards-rear-view mirror, towards the dash board, etc. This type of information is expected to be useful in conjunction with other sensors to enable safer driving.
