Real-time 3-D face tracking and modeling from awebcam

Choi, Jongmoo; Dumortier, Yann; Choi, Sang‐Il; Ahmad, Muhammad Bilal; Medioni, Gérard

doi:10.1109/wacv.2012.6163031

Cited by 12 publications

(14 citation statements)

References 21 publications

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“…We first review the previous work of 3-D Face Tracker [10]. The process includes three main steps: initialization, tracking, and re-acquisition.…”

Section: Previous Workmentioning

confidence: 99%

“…In [10], the authors use Speeded Up Robust Features (SURF) keypoints [9] because they are informative. However, this approach has many weaknesses.…”

Section: Dynamic Random Points Strategymentioning

confidence: 99%

“…3-D Face Tracker [10] is one of the state of the art works. By modeling a users 3-D face and using it as a reference to track 2-D & 3-D correspondences, the research proposes an effective solution for 3-D head pose estimation.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we have extended the previous 3-D face tracking and modeling framework [10] with automatic initialization, reacquisition, and automatic pose correction. Our system first generates a 3-D face model from a single frontal image.…”

mentioning

confidence: 99%

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Real-time 3-D face tracking and modeling framework for mid-res cam

Choi

Tran

Dumortier

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

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We present a robust, real-time 3-D face tracking and modeling system providing accurate 6 degree-of-freedom head pose in the presence of large out-of-plane motion, strong expression changes, and partial occlusions. In this paper, we have extended the previous 3-D face tracking and modeling framework [10] with automatic initialization, reacquisition, and automatic pose correction. Our system first generates a 3-D face model from a single frontal image. We then extract uniformly distributed random points and track them in 2-D. Given these correspondences, the 3-D head pose is robustly estimated using a RANSAC-PnP process. As the head moves, we dynamically add new feature points to handle a large range of poses. A measure of the accumulated error over time allows an auto-correction mechanism to recover from drift when necessary. If the tracker gets lost, due to motion blur or strong occlusions, the system re-initializes. We present live demo results, which shows excellent tracking under large motion (roll: 360 • , yaw: ±90 • , pitch: -60 • to +90 • ), fast movement, occlusion and facial expression variations. The system runs at 14 fps on a laptop CPU. By experiments on different datasets, our method shows state of the art results.

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“…We first review the previous work of 3-D Face Tracker [10]. The process includes three main steps: initialization, tracking, and re-acquisition.…”

Section: Previous Workmentioning

confidence: 99%

“…In [10], the authors use Speeded Up Robust Features (SURF) keypoints [9] because they are informative. However, this approach has many weaknesses.…”

Section: Dynamic Random Points Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Real-time 3-D face tracking and modeling framework for mid-res cam

Choi

Tran

Dumortier

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

Self Cite

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show abstract

“…First, we use a real-time 3D head tracking module, which was developed in our lab [4], to track a person's head in 3D (6 degrees of freedom). We use a RGB video as the input, detect frontal faces [5,6], extract facial landmarks from a neutral face [7][8][9][10], deform a 3D generic face model to fit the input face [3,4,10], and track the 3D head motion from the video using the updated 3D face.…”

Section: Introductionmentioning

confidence: 99%

3D Facial Landmark Tracking and Facial Expression Recognition

Medioni¹,

Choi

Labeau³

et al. 2013

Journal of information and communication convergence engineerin

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In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person's head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.

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3D facial motion tracking by combining online appearance model and cylinder head model in particle filtering

Wang

2014

Sci. China Inf. Sci.

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Variable structure multiple model for articulated human motion tracking from monocular video sequences SCIENCE CHINA Information Sciences 55, 1138 (2012); 3D textured model encryption via 3D Lu chaotic mapping SCIENCE CHINA Information Sciences 60, 122107 (2017); A 3D bounding surface model for rockfill materials SCIENCE CHINA Technological Sciences 54, 2904 (2011);. BRIEF REPORT. SCIENCE CHINA Information Sciences

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Real-time 3-D face tracking and modeling from awebcam

Cited by 12 publications

References 21 publications

Real-time 3-D face tracking and modeling framework for mid-res cam

Real-time 3-D face tracking and modeling framework for mid-res cam

3D Facial Landmark Tracking and Facial Expression Recognition

3D facial motion tracking by combining online appearance model and cylinder head model in particle filtering

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