The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that explicitly models part occlusion. The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances. This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model. We test the model on several benchmarks for landmark localization and detection including challenging new data sets featuring significant occlusion. We find that the addition of an explicit occlusion model yields a detection system that outperforms existing approaches for occluded instances while maintaining competitive accuracy in detection and landmark localization for unoccluded instances.
Recently, approaches based on pose regression , which train regressors that predict landmark locations from both appearance and spatial context provided by other detector responses, has also shown impressive performance @cite_37 @cite_32 @cite_39 @cite_21 @cite_2 @cite_33 @cite_16 @cite_13 @cite_30 . While these approaches lack an explicit model of face shape, stage-wise pose-regression models can be trained efficiently in a discriminative fashion and thus sidestep the optimization problems of global model alignment while providing fast, feed-forward performance at test time.
@cite_30 @cite_37 @cite_33 @cite_21 @cite_32 @cite_39 @cite_2 @cite_16 @cite_13
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Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-feature-based facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point's location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors. Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patches and the location of feature points from the entire set of faces, conditional regression forest learn the relations conditional to global face properties. In our experiments, we use the head pose as a global property and demonstrate that conditional regression forests outperform regression forests for facial feature detection. We have evaluated the method on the challenging Labeled Faces in the Wild [20] database where close-to-human accuracy is achieved while processing images in real-time. Facial landmark detection is a fundamental step for many tasks in computer vision such as expression recognition and face alignment. In this paper, we focus on the detection of landmarks under realistic scenarios that include pose, illumination and expression challenges as well as blur and low-resolution input. In our approach, an n-point shape of point-landmarks is represented as a union of simpler polygonal sub-shapes. The core idea of our method is to find the sequence of deformation parameters simultaneously for all sub-shapes that transform each point-landmark into its target landmark location. To accomplish this task, we introduce an agglomerate of fern regressors. To optimize the convergence speed and accuracy we take advantage of search localization using component-landmark detectors, multi-scale analysis and learning of point cloud dynamics. Results from extensive experiments on facial images from several challenging publicly available databases demonstrate that our method (ACFeR) can reliably detect landmarks with accuracy comparable to commercial software and other state-of-the-art methods. We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a non-parametric set of global models for the part locations based on over one thousand hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting and occlusion than prior ones. We show excellent performance on a new dataset gathered from the internet and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset. This paper presents a highly efficient, very accurate regression approach for face alignment. Our approach has two novel components: a set of local binary features, and a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. Our approach achieves the state-of-the-art results when tested on the current most challenging benchmarks. Furthermore, because extracting and regressing local binary features is computationally very cheap, our system is much faster than previous methods. It achieves over 3, 000 fps on a desktop or 300 fps on a mobile phone for locating a few dozens of landmarks.