Face alignment consists in aligning a shape model on a face in an image. It is an active domain in computer vision as it is a preprocessing for applications like facial expression recognition, face recognition and tracking, face animation, etc. Current state-of-the-art methods already perform well on "easy" datasets, i.e. those that present moderate variations in head pose, expression, illumination or partial occlusions, but may not be robust to "in-the-wild" data. In this paper, we address this problem by using an ensemble of deep regressors instead of a single large regressor. Furthermore, instead of averaging the ouputs of each regressor, we propose an adaptative weighting scheme that uses a tree-structured gate. Experiments on several challenging face datasets demonstrate that our approach outperforms the state-of-the-art methods.
We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data. A key aspect of our approach is that errors can be backpropagated through a complete landmark localization model. Second, we propose and explore an unsupervised learning technique for landmark localization based on having a model predict equivariant landmarks with respect to transformations applied to the image. We show that these techniques, improve landmark prediction considerably and can learn effective detectors even when only a small fraction of the dataset has landmark labels. We present results on two toy datasets and four real datasets, with hands and faces, and report new state-of-the-art on two datasets in the wild, e.g. with only 5 of labeled images we outperform previous state-of-the-art trained on the AFLW dataset. We address the problem of robust facial feature localization in the presence of occlusions, which remains a lingering problem in facial analysis despite intensive long-term studies. Recently, regression-based approaches to localization have produced accurate results in many cases, yet are still subject to significant error when portions of the face are occluded. To overcome this weakness, we propose an occlusion-robust regression method by forming a consensus from estimates arising from a set of occlusion-specific regressors. That is, each regressor is trained to estimate facial feature locations under the precondition that a particular pre-defined region of the face is occluded. The predictions from each regressor are robustly merged using a Bayesian model that models each regressor’s prediction correctness likelihood based on local appearance and consistency with other regressors with overlapping occlusion regions. After localization, the occlusion state for each landmark point is estimated using a Gaussian MRF semi-supervised learning method. Experiments on both non-occluded and occluded face databases demonstrate that our approach achieves consistently better results over state-of-the-art methods for facial landmark localization and occlusion detection. In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attributes, such as gender, expression, and appearance attributes. This is non-trivial since different attribute inference tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, which not only learns the inter-task correlation but also employs dynamic task coefficients to facilitate the optimization convergence when learning multiple complex tasks. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing face alignment methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art methods based on cascaded deep model.