Robust 3D head tracking by online feature registration

Jang, Jun-Su; Kanade, Takeo

doi:10.1109/afgr.2008.4813307

Cited by 41 publications

(33 citation statements)

References 18 publications

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“…Two sets of head pose data are provided based on 3D and 2D modalities. In 2D texture sequence, head pose was measured from the 2D videos using a cylindrical head tracker [19]. This tracker is personindependent, robust, and has concurrent validity with a personspecific 2D + 3D AAM [20] and with a magnetic motion capture device [19].…”

Section: Head Pose Trackingmentioning

confidence: 99%

BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

Zhang

Liu

Cohn

et al. 2014

Image and Vision Computing

611

367

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Section: Head Pose Trackingmentioning

confidence: 99%

BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

Zhang

Liu

Cohn

et al. 2014

Image and Vision Computing

611

367

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“…For model based approaches, a popular method is to use a 2D model for the face [5,13,9,14], but these models only approximate the shape of the face at near frontal head pose. Alternatively, various 3D head models can be used [7,1,12,4,2]. These more complex models require accurate initialization and are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Feature selection of facial displays for detection of non verbal communication in natural conversation

Sheerman-Chase

Ong

Bowden

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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This paper presents a head pose and facial feature estimation technique that works over a wide range of pose variations without a priori knowledge of the appearance of the face. Using simple LK trackers, head pose is estimated by Levenberg-Marquardt (LM) pose estimation using the feature tracking as constraints. Factored sampling and RANSAC are employed to both provide a robust pose estimate and identify tracker drift by constraining outliers in the estimation process. The system provides both a head pose estimate and the position of facial features and is capable of tracking over a wide range of head poses.

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“…Gaurav et al [10] showed 3D pose estimation using a particle filter without known camera focal length. Jang and Kanade [11] used SIFT and normalized correlation methods to extract and match the feature points.…”

Section: Introductionmentioning

confidence: 99%

On using gait biometrics to enhance face pose estimation

Jung

Nixon

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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Abstract-Many face biometrics systems use controlled environments where subjects are viewed directly facing the camera. This is less likely to occur in surveillance environments, so a process is required to handle the pose variation of the human head, change in illumination, and low frame rate of input image sequences. This has been achieved using scale invariant features and 3D models to determine the pose of the human subject. Then, a gait trajectory model is generated to obtain the correct the face region whilst handing the looming effect. In this way, we describe a new approach aimed to estimate accurate face pose. The contributions of this research include the construction of a 3D model for pose estimation from planar imagery and the first use of gait information to enhance the face pose estimation process.

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Robust 3D head tracking by online feature registration

Abstract: Abstract

Cited by 41 publications

References 18 publications

BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

Feature selection of facial displays for detection of non verbal communication in natural conversation

On using gait biometrics to enhance face pose estimation

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