Supervised descent method and its applications to face alignment

Supervised descent method and its applications to face alignment

Supervised Descent Method SDM is an efficient and accurate approach for facial landmark locating and face alignment. In the training phase, it requires a large amount of training samples to learn the descent directions and get the corresponding regressors.

Then in the test phase, it uses the corresponding regressors to estimate the descent directions and locate the facial landmarks. However, when the facial expression or direction changes too much, generally SDM cannot obtain good performance due to the large variation between the initial shape the initial shape of SDM is the mean shape of the training samples and the target shape.

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Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos

International Conference on Intelligent Computing. Conference paper First Online: 24 July This is a preview of subscription content, log in to check access.

Chen, C. In: International Conference on Biometrics, pp. Ashraf, A. Image Vis.

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Thies, J. In: Computer Vision and Pattern Recognition, pp. Datta, A. In: Face and Gesturepp. Xiong, X. Cootes, T. Image Underst. IEEE Trans. Pattern Anal. Cristinacce, D. In: British Machine Vision Conference, pp. Dollar, P. Cao, X. Zhu, S. Ren, S. Sun, Y. Wu, Y. Zhang, K. Dong, X. Wu, W. Zhang, Z.Supervised Descent Method SDM is one of the leading cascaded regression approaches for face alignment with state-of-the-art performance and a solid theoretical basis.

However, SDM is prone to local optima and likely averages conflicting descent directions. This makes SDM ineffective in covering a complex facial shape space due to large head poses and rich non-rigid face deformations. The optimization space is first partitioned with regard to shape variations using k-means. The generated subspaces show semantic significance which highly correlates with head poses.

Faces among a certain subspace also show compatible shape-appearance relationships. Then, Naive Bayes is applied to conduct robust subspace prediction by concerning about the relative proximity of each subspace to the sample.

This guarantees that each sample can be allocated to the most appropriate subspace-specific regressor. The proposed method is validated on benchmark face datasets with a mobile facial tracking implementation.

Face alignment aims to automatically localize fiducial facial points or landmarks. It is a fundamental step for many facial analysis tasks, e.

These tasks are essential to Human-System Interaction HSI applications including driver-car interaction, human-robot interaction and mobile applications.

The field of face alignment has witnessed rapid progresses in recent years, especially with the application and development of cascaded regression methods [ 26273839 ].

This kind of methods typically learns a sequence of descent directions from image features that move an initial shape towards the ground truth iteratively. Among various cascaded regression approaches for face alignment, SDM [ 27 ] has risen as one of the most popular approaches due to its high efficiency and the state-of-the-art performance. However, SDM has two main drawbacks: 1 It highly relies on the initialization and is prone to local optima. If the initialised shape is far away from the target shape, the algorithm is prone to a poor local optimum see Fig.

Top row: initial shape, bottom row: results after four iterations. Red points: predicted landmarks, green points: ground-truth landmarks. Actually, only if initial points are close to each other and also target at the same destination, then the compatible descent directions can be learned via SDM.

However, this strong prerequisite is very difficult to meet in face alignment, since face images vary from head poses and facial expressions, which are supposed to have different shape-feature relationships.

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This also leads to another issue of SDM: the algorithm is derived on a weak assumption that the non-linear feature extraction function e. SIFT [ 13 ] or [ 17 ] is identical for all the face images. As stated in [ 28 ], the feature extraction function is parameterized not only by facial landmark locations, but also by the images such as faces with different head poses and different subjects.

It can be inferred that one possible cause of above issues is that the face alignment task occupies multiple optimization subspaces, but these subspaces cannot be explained within a single optimization process.

supervised descent method and its applications to face alignment

Although SDM has been extensively studied and further developed in the past few years, there are few works on this essential but relatively unexplored problem [ 828293235 ].Source: pdf. Abstract: Many computer vision problems e.

It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization ofa general smoothfunction. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: 1 The function might not be analytically differentiable and numerical approximations are impractical. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points.

We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-ofthe-art performance in the problem of facial feature detec- tion.

The code is available at www. Reference: text. Abstract: Facial feature tracking is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, sincefaces may have varyingfacial expressions, poses or occlusions. In this paper, we address this problem by proposing a face shape prior model that is constructed based on the Restricted Boltzmann Machines RBM and their variants.

Specifically, we first construct a model based on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal view.

To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models with image measurements of facial feature points.

Experiments on benchmark databases show that with the proposed method, facial feature points can be tracked robustly and accurately even if faces have significant facial expressions and poses. Abstract: Making a high-dimensional e. This prevents further exploration of the use of a highdimensional feature. In this paper, we study the performance of a highdimensional feature. We first empirically show that high dimensionality is critical to high performance.

A K-dim feature, based on a single-type Local Binary Pattern LBP descriptor, can achieve significant improvements over both its low-dimensional version and the state-of-the-art. We also make the high-dimensional feature practical. With our proposed sparse projection method, named rotated sparse regression, both computation and model storage can be reduced by over times without sacrificing accuracy quality.

Abstract: Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplarbased face detector that integrates image retrieval and discriminative learning.

A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique.

As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations.

Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. The same methodology can also be easily generalized to other facerelated tasks, such as attribute recognition, as well as general object detection.

The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps.

Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. Moreover, the DRMF method is computationally very efficient and is real-time capable.Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Many computer vision problems e.

Supervised Descent Method (SDM) applied to face alignment

It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. View on IEEE. Save to Library. Create Alert. Launch Research Feed. Share This Paper.

Supervised Descent Method and Its Applications to Face Alignment

Supplemental Presentations. Explore Further Discover more papers related to the topics discussed in this paper. Figures, Tables, and Topics from this paper. Figures and Tables. Explore key concepts Links to highly relevant papers for key concepts in this paper: EigenTracking Active Appearance Models. Constrained local model YouTube Celebrities. Citations Publications citing this paper. References Publications referenced by this paper.

Face alignment through subspace constrained mean-shifts Jason M. SaragihSimon LuceyJeffrey F. Learning deformable shape manifolds Samuel RiveraAleix M. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

First, I have to detect the face and pre-process the image. Can't you then use another Haar classifier to find each eye eyes are very easy to find then assuming the person has two eyes and we define a 'level' face to mean the eyes are horizontal.

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Finding the accurate position of the eyes in a given image is far from trivial. The Haar-cascades for finding the eyes in OpenCV produce too many false positive to be useful, moreover this approach won't be robust to image rotation it may compensate slight rotation though, I don't know the training images. You'll need a robust head pose estimation for aligning face images.

I did some research myself and I think sharing algorithms and code is useful here.

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The most interesting approaches I have seen are:. Gary B. Unsupervised joint alignment of complex images. ZhuD. I tried the following face alignment code from the Labelled Faces in the Wild project page. It works really well and does not require detecting facial feature points. If you still wish to find face key points, I find that the Viola-Jones detector is not very robust and accurate.

C code can be downloaded from the above site.

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Xiong and F. It is extremely fast and effective. You can check their project website IntraFace. They provide an easy-to-use software. However, the core part code, i. Detecting misaligned faces make face recognition difficult. Sometimes you want to fix the alignment, sometimes it is sufficient to exclude the ones that aren't aligned correctly for instance if you're detecting faces in a video stream. I took the latter approach and trained a special Haar Cascade to only detect correctly aligned, well-lit faces.

If you use my cascade let me know how it works for you. I'm curious what results others would obtain. It met my needs.

It is fast and very accurate. It supports faces that are tilted at an angle larger than 45 degrees. As for how to implement in code, that's another problem that I'm currently working on, but at least this is a starting point.

Learn more. Asked 8 years, 3 months ago. Active 1 year, 7 months ago. Viewed 16k times. For face detection I have used the HaarCascadeClassifier.This is because the uncertainty of the estimated 2D landmarks will affect the quality of face reconstruction.

In this paper, we propose a novel joint 2D and 3D optimization method to adaptively reconstruct 3D face shapes from a single image, which combines the depths of 3D landmarks to solve the uncertain detections of invisible landmarks. The strategy of our method involves two aspects: a coarse-to-fine pose estimation using both 2D and 3D landmarks, and an adaptive 2D and 3D re-weighting based on the refined pose parameter to recover accurate 3D faces.

Experimental results on multiple datasets demonstrate that our method can generate high-quality reconstruction from a single color image and is robust for self-occlusion and large poses. Kun Li. Jing Yang. Nianhong Jiao. Jinsong Zhang. Yu-Kun Lai. This paper presents a new system to obtain dense object reconstructions Three-dimensional shape reconstruction of 2D landmark points on a single State-of-the-art methods for 3D reconstruction of faces from a single im Recently, it was shown that excellent results can be achieved in both fa Recently, 3D face reconstruction from a single image has achieved great In this paper, we address the problem of estimating a 3D human pose from Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

Human reconstruction from images, especially for faces, is an important and challenging problem, which has drawn much attention from both academia and industry [ 1234 ]. Although existing face reconstruction methods based on multiple images have achieved promising results, it is still a tough problem for a single input image, especially under partial occlusions and extreme poses.

To fit 3DMM to a facial image with self-occlusions or large poses, Zhu. Inspired by recent work on 3D landmark detection, we use the depth information of 3D landmarks together with 2D landmarks to resolve the inherent depth ambiguities of the re-projection constraint during 3D face reconstruction by joint 2D and 3D optimization.

It is hard to decide which detected landmarks are more believable. In order to effectively combine 2D and 3D landmarks, we propose a 2D and 3D re-weighting method to adaptively adjust the weights of 2D and 3D landmarks. In addition, instead of solving pose parameters directly, we design a coarse-to-fine method for accurate face pose estimation.

Our method does not need manual intervention, and is robust to extreme poses and partial occlusions. Joint 2D and 3D optimization. We formulate the 3D face reconstruction problem in a unified joint 2D and 3D optimization framework. To our best knowledge, our method is the first optimization method using both 2D and 3D information for face reconstruction. Our method is fully automatic and robust to extreme poses and partial occlusions.

Coarse-to-fine pose estimation. To obtain accurate pose parameters for face reconstruction, we propose a coarse-to-fine scheme using both 2D and 3D landmarks.In this paper, we propose a spatial-temporal relational reasoning networks STRRN approach to investigate the problem of omni-supervised face alignment in videos. Unlike existing fully supervised methods which rely on numerous annotations by hand, our learner exploits large scale unlabeled videos plus available labeled data to generate auxiliary plausible training annotations.

Motivated by the fact that neighbouring facial landmarks are usually correlated and coherent across consecutive frames, our approach automatically reasons about discriminative spatial-temporal relationships among landmarks for stable face tracking. Specifically, we carefully develop an interpretable and efficient network module, which disentangles facial geometry relationship for every static frame and simultaneously enforces the bi-directional cycle-consistency across adjacent frames, thus allowing the modeling of intrinsic spatial-temporal relations from raw face sequences.

supervised descent method and its applications to face alignment

Extensive experimental results demonstrate that our approach surpasses the performance of most fully supervised state-of-the-arts. Congcong Zhu. Hao Liu. Zhenhua Yu. Xuehong Sun. In this paper, we propose a novel feature learning framework for video p Face anti-spoofing is significant to the security of face recognition sy Heart rate HR is an important physiological signal that reflects the p Editing faces in videos is a popular yet challenging aspect of computer In recent years, heatmap regression based models have shown their effect Tracking Facial Points in unconstrained videos is challenging due to the Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

The major reasons are two-fold: 1 Existing methods intensely rely on the sheer volume of training annotations. Hence, we are supposed to propose an accurate and robust face alignment algorithm to automatically annotate the large amounts of unlabeled face videos. However, these fully-supervised methods require large amounts of precise hand-crafted annotations for training and cannot be directly adopted for unlabeled data.

supervised descent method and its applications to face alignment

One major issue in this type of method is that the training efficiency still relies on the tremendous volume of per-frame annotations, where it is challenging to manually annotate numerous frames.

To address this issue, we investigate into the omni- supervised learning towards face alignment inspired by. To produce plausible training annotations for model update, our approach reasons about meaningful relations of unlabeled videos in both the spatial and temporal dimensions accordingly. Then our STRRN disentangles the component-based appearance and geometric information with preserving a dendritic structure.

For modeling of the temporal relation, we ensure that our model tracks forward until the target and it should arrive the starting position in the backward order.

This principally enforces the cycle-consistent temporal relation on consecutive frames for reliable tracking. To learn the network parameters, we adopt a cooperative and competitive strategy to exploit the complementary information from both the tracking module and the backbone detector.

To evaluate the effectiveness of our proposed approach, we carry out extensive experiments on folds of large scale unlabeled video datasets and experimental results indicate compelling performance versus state-of-the-art methods. We briefly review some related literatures of existing face alignment methods and knowledge distillation models. Conventional Face Alignment: Existing face alignment methods are roughly classified into image-based.

For each routing, our architecture incorporates with the spatial and temporal modules.