Learning to simultaneously handle face alignment of arbitrary views, e.g. frontal and profile views, appears to be more challenging than we thought. The difficulties lay in i) accommodating the complex appearance-shape relations exhibited in different views, and ii) encompassing the varying landmark point sets due to self-occlusion and different landmark protocols. Most existing studies approach this problem via training multiple viewpoint-specific models, and conduct head pose estimation for model selection. This solution is intuitive but the performance is highly susceptible to inaccurate head pose estimation. In this study, we address this shortcoming through learning an Ensemble of Model Recommendation Trees (EMRT), which is capable of selecting optimal model configuration without prior head pose estimation. The unified framework seamlessly handles different viewpoints and landmark protocols, and it is trained by optimising directly on landmark locations, thus yielding superior results on arbitrary-view face alignment. This is the first study that performs face alignment on the full AFLWdataset with faces of different views including profile view. State-of-the-art performances are also reported on MultiPIE and AFW datasets containing both frontaland profile-view faces.
. This study extends @cite_19 by iteratively re-estimating the head pose and facial expression based on the current estimated shape, and then re-estimating the facial landmarks with the updated prior model probability. Further improvement is achieved in this work compared to @cite_19 . Similar to @cite_19 , the algorithm begins with head pose estimation based on global appearance. When applied to faces with large pose variation, if the initial head pose estimation is incorrect, the error is rather hard to be rectified during iterative optimisation since the initial shape is estimated with the wrong prior probability.
@cite_19
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Fast and reliable algorithms for estimating the head pose are essential for many applications and higher-level face analysis tasks. We address the problem of head pose estimation from depth data, which can be captured using the ever more affordable 3D sensing technologies available today. To achieve robustness, we formulate pose estimation as a regression problem. While detecting specific face parts like the nose is sensitive to occlusions, learning the regression on rather generic surface patches requires enormous amount of training data in order to achieve accurate estimates. We propose to use random regression forests for the task at hand, given their capability to handle large training datasets. Moreover, we synthesize a great amount of annotated training data using a statistical model of the human face. In our experiments, we show that our approach can handle real data presenting large pose changes, partial occlusions, and facial expressions, even though it is trained only on synthetic neutral face data. We have thoroughly evaluated our system on a publicly available database on which we achieve state-of-the-art performance without having to resort to the graphics card. We present a hierarchical graphical model to probabilistically estimate head pose angles from real-world videos, that leverages the temporal pose information over video frames. The proposed model employs a number of complementary facial features, and performs feature level, probabilistic classifier level and temporal level fusion. Extensive experiments are performed to analyze the pose estimation performance for different combination of features, different levels of the proposed hierarchical model and for different face databases. Experiments show that the proposed head pose model improves on the current state-of-the-art for the unconstrained McGillFaces [10] and the constrained CMU Multi-PIE [14] databases, increasing the pose classification accuracy compared to the current top performing method by 19.38 and 19.89 , respectively. Automatic head pose estimation from real-world video sequences is of great interest to the computer vision community since pose provides prior knowledge for tasks, such as face detection and classification. However, developing pose estimation algorithms requires large, labeled real-world video databases on which computer vision systems can be trained and tested. Manual labeling of each frame is tedious, time consuming, and often difficult due to the high uncertainty in head pose angle estimate, particularly in unconstrained environments that include arbitrary facial expression, occlusion, illumination etc. To overcome these difficulties, a semi-automatic framework is proposed for labeling temporal head pose in real-world video sequences. The proposed multi-stage labeling framework first detects a subset of frames with distinct head poses over a video sequence, which is then manually labeled by the expert to obtain the ground truth for those frames. The proposed framework provides a continuous head pose label and corresponding confidence value over the pose angles. Next, the interpolation scheme over a video sequence estimates i) labels for the frames without manual labels and ii) corresponding confidence values for interpolated labels. This confidence value permits an automatic head pose estimation framework to determine the subset of frames to be used for further processing, depending on the labeling accuracy required. The experiments performed on an in-house, labeled, large, real-world face video database (which will be made publicly available) show that the proposed framework achieves 96.98 labeling accuracy when manual labeling is only performed on 30 of the video frames. Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regressive neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth model for learning image faces from depth. Results in face reconstruction are qualitatively impressive. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second.