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Lucas-Kanade and active appearance models are among the most commonly used methods for image alignment and facial fitting, respectively. They both utilize nonlinear gradient descent, which is usually applied on intensity values. In this paper, we propose the employment of highly descriptive, densely sampled image features for both problems. We show that the(More)
Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despite the success of cascaded regression for problems such as(More)
A B S T R A C T Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues. (a) Most existing databases,(More)
The Menpo Project, hosted at http://www.menpo.io, is a BSD-licensed software platform providing a complete and comprehensive solution for annotating, building, fitting and evaluating deformable visual models from image data. Menpo is a powerful and flexible cross-platform framework written in Python that works on Linux, OS X and Windows. Menpo has been(More)
In this paper we present a novel generative deformable model motivated by Pictorial Structures (PS) and Active Appearance Models (AAMs) for object alignment in-the-wild. Inspired by the tree structure used in PS, the proposed Active Pictorial Structures (APS)<sup>1</sup> model the appearance of the object using multiple graph-based pairwise normal(More)
We propose the combination of dense Histogram of Oriented Gradients (HOG) features with Active Appearance Models (AAMs). We employ the efficient Inverse Compositional optimization technique and show results for the task of face fitting. By taking advantage of the descriptive characteristics of HOG features, we build robust and accurate AAMs that generalize(More)
Generic face detection and facial landmark localization in static imagery are among the most mature and well-studied problems in machine learning and computer vision. Currently, the top performing face detectors achieve a true positive rate of around 75-80% whilst maintaining low false positive rates. Furthermore, the top performing facial landmark(More)
Deformable objects are everywhere. Faces, cars, bicycles, chairs etc. Recently, there has been a wealth of research on training deformable models for object detection, part localization and recognition using annotated data. In order to train deformable models with good generalization ability, a large amount of carefully annotated data is required, which is(More)
We propose an Unsupervised method for Extreme States Classification (UnESC) on feature spaces of facial cues of interest. The method is built upon Active Appearance Models (AAM) face tracking and on feature extraction of Global and Local AAMs. UnESC is applied primarily on facial pose, but is shown to be extendable for the case of local models on the eyes(More)
The predominant strategy for facial expressions analysis and temporal analysis of facial events is the following: a generic facial landmarks tracker, usually trained on thousands of carefully annotated examples , is applied to track the landmark points, and then analysis is performed using mostly the shape and more rarely the facial texture. This paper(More)