HOG active appearance models

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 well to unseen faces with illumination, identity, pose and occlusion variations. Our experiments on challenging in-the-wild databases show that HOG AAMs significantly outperform current state-of-the-art results of discriminative methods trained on larger databases

Linear Facial Expression Transfer With Active Appearance Models

The issue of transferring facial expressions from one person's face to another's has been an area of interest for the movie industry and the computer graphics community for quite some time. In recent years, with the proliferation of online image and video collections and web applications, such as Google Street View, the question of preserving privacy through face de-identification has gained interest in the computer vision community. In this paper, we focus on the problem of real-time dynamic facial expression transfer using an Active Appearance Model framework. We provide a theoretical foundation for a generalisation of two well-known expression transfer methods and demonstrate the improved visual quality of the proposed linear extrapolation transfer method on examples of face swapping and expression transfer using the AVOZES data corpus. Realistic talking faces can be generated in real-time at low computational cost

Prostate MR image segmentation using 3D active appearance models

This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain the point correspondence between the given training cases. Subsequently, an AAM is used to segment the prostate on 50 training cases. The method is evaluated using a 5-fold cross validation over 5 repetitions. The mean Dice similarity coefficient and 95% Hausdorff distance are 0.78 and 7.32 mm respectively

A Unified Framework for Compositional Fitting of Active Appearance Models

Active Appearance Models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision. In this paper, we study the problem of fitting AAMs using Compositional Gradient Descent (CGD) algorithms. We present a unified and complete view of these algorithms and classify them with respect to three main characteristics: i) cost function; ii) type of composition; and iii) optimization method. Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost function that can be interpreted as a general probabilistic formulation of the well-known project-out loss; b) introducing two new types of composition, asymmetric and bidirectional, that combine the gradients of both image and appearance model to derive better conver- gent and more robust CGD algorithms; and c) providing new valuable insights into existent CGD algorithms by reinterpreting them as direct applications of the Schur complement and the Wiberg method. Finally, in order to encourage open research and facilitate future comparisons with our work, we make the implementa- tion of the algorithms studied in this paper publicly available as part of the Menpo Project.Comment: 39 page

Segmentation of Radiographs of Hands with Joint Damage Using Customized Active Appearance Models

This paper is part of a project that investigates the possibilities of automating the assessment of joint damagein hand radiographs. Our goal is to design a robust segmentationalgorithm for the hand skeleton. The algorithm is\ud based on active appearance models (AAM) [1], which have been used for hand segmentation before [2]. The results will be used in the future for radiographic assessment of rheumatoid arthritis and the early detection of joint damage. New in this work with respect to [2] is the use of multiple object warps for each individual bone in a single AAM. This method prevents modelling and reconstruction defects caused when warping overlapping objects. This makes the algorithm more robust in cases where joint damage is present. The current implementation of the model includes the metacarpals, the phalanges, and the carpal region. For a first experimental evaluation a collection of 50 hand radiographs has been gathered. The image data set was split into a training set (40) and a test set (10) in order to evaluate the algorithm’s performance. First results show that in 8 images from the test set the bone contours are detected correctly within 1.3 mm (1 STD) at 15 pixels/cm resolution. In two images not all contours are detected correctly. Possibly this is caused by extreme deviations in these images that have not yet been incorporated in the model due to a limited training set. More training examples are needed to optimize the AAM and improve the quality and reliability of the results

Adapted active appearance models

Active Appearance Models (AAM) are able to align known faces in an efficient manner when face pose and illumination are controlled. The AAM exploit a set of face examples in order to extract a statistical model. There is no difficulty to align a face with the same type (same morphology, illumination and pose) which constitute the example data set. Unfortunately, the AAM are less outstanding from the moment when the illumination, pose and face type changes. AAM robustness is link to the variability introduced in the learning base. The more the AAM will contain variability, the more it will be able to adapt itself to variable faces with the following drawback : the data represented in the reduced parameters space will then form different classes letting appear holes, regions without any data (see Fig. 1). It is therefore very difficult to make the AAM converge in this scattered space. We propose in this paper a robust Active Appearance Models allowing a real-time implementation. To increase the AAM robustness to illumination changes, we propose Oriented Map AAM (OM-AAM). Adapted AAM will be presented after to increase the AAM robustness to any other types of variability (in identity, pose, expression etc.)...Les Modèles Actifs d’Apparence (MAA) sont efficaces lorsqu’il s’agit d’aligner (détecter les contours des yeux, du nez et de la bouche) des visages connus dans des espaces contraints (illumination et pose contrôlées). Nous proposons des Modèles Actifs d’Apparence Adaptés afin d’aligner des visages inconnus dans des poses et illuminations quelconques. Notre proposition repose d’une part sur une transformation des textures du modèle actif en carte d’orientation, ce qui impacte l’opération de normalisation des MAA ; et d’autre part sur une recherche dans une banque de modèles pré-calculés du MAA le plus adapté au visage inconnu. Des tests sur des bases publiques et privées (BioId, CMU-PIE) montrent l’intérêt de notre approche: il devient possible d’aligner en temps réel des visages inconnus dans des situations où la lumière et la pose sont non contrôlées