3,128 research outputs found
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Facial landmark detection, head pose estimation, and facial deformation
analysis are typical facial behavior analysis tasks in computer vision. The
existing methods usually perform each task independently and sequentially,
ignoring their interactions. To tackle this problem, we propose a unified
framework for simultaneous facial landmark detection, head pose estimation, and
facial deformation analysis, and the proposed model is robust to facial
occlusion. Following a cascade procedure augmented with model-based head pose
estimation, we iteratively update the facial landmark locations, facial
occlusion, head pose and facial de- formation until convergence. The
experimental results on benchmark databases demonstrate the effectiveness of
the proposed method for simultaneous facial landmark detection, head pose and
facial deformation estimation, even if the images are under facial occlusion.Comment: International Conference on Computer Vision and Pattern Recognition,
201
Fine-Grained Head Pose Estimation Without Keypoints
Estimating the head pose of a person is a crucial problem that has a large
amount of applications such as aiding in gaze estimation, modeling attention,
fitting 3D models to video and performing face alignment. Traditionally head
pose is computed by estimating some keypoints from the target face and solving
the 2D to 3D correspondence problem with a mean human head model. We argue that
this is a fragile method because it relies entirely on landmark detection
performance, the extraneous head model and an ad-hoc fitting step. We present
an elegant and robust way to determine pose by training a multi-loss
convolutional neural network on 300W-LP, a large synthetically expanded
dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from
image intensities through joint binned pose classification and regression. We
present empirical tests on common in-the-wild pose benchmark datasets which
show state-of-the-art results. Additionally we test our method on a dataset
usually used for pose estimation using depth and start to close the gap with
state-of-the-art depth pose methods. We open-source our training and testing
code as well as release our pre-trained models.Comment: Accepted to Computer Vision and Pattern Recognition Workshops
(CVPRW), 2018 IEEE Conference on. IEEE, 201
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