A Review Article for Active Appearance Model Fitting Based on Optimization Methods
---
paper_title: Active appearance models

paper_content:

We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
---
paper_title: Face Recognition Technique Based on Active Appearance Model

paper_content:

A face recognition technique is one of the common computer applications that automatically identifies or authenticates a person from a digital image or a video frame in a video source. This face recognition procedure is done by matching the selected face features from the image and the face database. Several face recognition techniques recognize the faces by extracting features from an image of the subject's face. The main challenges in face recognition techniques are to extract distinctive features that are not affected by external variations. Active Appearance Model (AAM) is one of the popular solutions that able to extract face features by precise modelling of human faces under various physical and environmental circumstances. In active appearance model approach, fitting the model with target image is a challenging task. The previous works shows that optimization approach is appropriate to solve this problem. Nevertheless, another problem is applying the optimization itself. In this paper, a face recognition technique based on AAM is proposed. In the proposed technique, the fitting problem of AAM is resolved by presenting ABC algorithm. The human face dataset called CASIA is utilized to analyse the performance of the proposed.
---
paper_title: Facial Feature Alignment by Manifold Learning of Active Appearance Model

paper_content:

Extracting accurate positions of eyes, nose and mouth, is a crucial process for face recognition and facial expression recognition. Classical methods such as Active Appearance Model (AAM) use the principal component analysis to reduce the dimensionality of appearance data, and an iterative search to find facial features by minimizing an error criteria of the reduced appearance data. In this paper, we propose a facial feature extraction approach by manifold learning. The manifold learning method, locality preserving projection (LPP), projects appearance data into low-dimensional data by considering neighborhood relation but not variance. The LPP can preserve local structure of appearance data, and remain most of the important characteristics of the appearance data. During search phase, AdaBoost face detection algorithm is utilized to locate the face localization, which can improve the search. Experimental data includes 870 images from AR face database which includes variations of illumination and expression, and 200 images from CMU PIE face database which includes different poses. Experimental results show that the proposed method has better performance than that of the AAM method.
---
paper_title: A comparative study of active appearance model annotation schemes for the face

paper_content:

Active appearance models (AAM), a group of flexible deformable models, have been widely used in various applications such as, object tracking, medical image segmentation and synthesis. AAMs are statistical models which model the shape and texture of an object. There has been much published work in this field to improve the speed and fitting accuracy. However, there has not been any significant study related to the quantity and selection of annotation points (landmarks) used to define the object and its texture. This paper proposes four different annotation schemes used for modeling the human face and evaluates each scheme in regard to reconstructing face images. In pursuit of choosing a particular annotation scheme for age progression and synthesis, this paper presents qualitative and quantitative methods for evaluation.
---
paper_title: Active appearance models

paper_content:

We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
---
paper_title: 3D face model reconstruction from single 2D frontal image

paper_content:

Modeling a 3D face model that resembles a human face has been an important issue in computer graphics. A number of researchers have proposed various modeling methodologies to model the human face realistically. However, despite the fact that these methods give high-quality results, the time, cost, and labor required to generate such models by these methods are often high. In this paper, we propose a fully automatic face modeling method that can rapidly create a realistic 3D face model on the basis of a single 2D image. We studied 200 frontal facial images and captured their statistical shape variations. Using these properties, facial features were detected from the images. The depth information of the facial features was then estimated using an anthropometrical measurement database of human faces. Finally, by aligning the feature points three-dimensionally and interpolating them, a facial surface is generated. Finally, upon these 3-dimensional alignment of the facial features, a surface interpolates these feature points are created. As a result, we could obtain realistic 3D faces models of several individuals without any efforts. Further, the computational cost of our method was much lower than that of other methods. Thus, our method showed remarkably fast performance.
---
paper_title: Segmented AAMs improve person-independent face fitting

paper_content:

An Active Appearance Model (AAM) is a variable shape and appearance model built from annotated training images. It has been largely used to synthesize or fit face images. Person-independent face AAM fitti ng is a challenging open issue. For standard AAMs, fitting a face image fo r an individual which is not in the training set is often limited in accuracy, thereby restricting the range of application. As a first contribution, we show that the limitation mainly co mes from the inability of the AAM appearance counterpart to generalize, i.e. to accurately generate previously unseen visual data. As a second contribution, we propose an efficient person-independent face fitting framework based on what we call multi-level segmented AAMs. Each segment encodes a physically meaningful part of the face, such as an eye. A coarse-to-fine fi tting strategy with a gradually increasing number of segments is used in order to ensure a large convergence basin. Fitting accuracy is assessed by comparison with manual labelling statistics constructed from multiple data annotations. Experimental results support the claim that standard AAMs are well-adapted to person-specific fitting while segmented AAMs outperform the classical AAMs in a personindependent context in terms of accuracy, and ability to generate new faces.
---
paper_title: Real-time combined 2D+3D active appearance models

paper_content:

Active appearance models (AAMs) are generative models commonly used to model faces. Another closely related types of face models are 3D morphable models (3DMMs). Although AAMs are 2D, they can still be used to model 3D phenomena such as faces moving across pose. We first study the representational power of AAMs and show that they can model anything a 3DMM can, but possibly require more shape parameters. We quantify the number of additional parameters required and show that 2D AAMs can generate model instances that are not possible with the equivalent 3DMM. We proceed to describe how a non-rigid structure-from-motion algorithm can be used to construct the corresponding 3D shape modes of a 2D AAM. We then show how the 3D modes can be used to constrain the AAM so that it can only generate model instances that can also be generated with the 3D modes. Finally, we propose a real-time algorithm for fitting the AAM while enforcing the constraints, creating what we call a "combined 2D+3D AAM".
---
paper_title: Tensor-Based AAM with Continuous Variation Estimation: Application to Variation-Robust Face Recognition

paper_content:

The active appearance model (AAM) is a well-known model that can represent a non-rigid object effectively. However, the fitting result is often unsatisfactory when an input image deviates from the training images due to its fixed shape and appearance model. To obtain more robust AAM fitting, we propose a tensor-based AAM that can handle a variety of subjects, poses, expressions, and illuminations in the tensor algebra framework, which consists of an image tensor and a model tensor. The image tensor estimates image variations such as pose, expression, and illumination of the input image using two different variation estimation techniques: discrete and continuous variation estimation. The model tensor generates variation-specific AAM basis vectors from the estimated image variations, which leads to more accurate fitting results. To validate the usefulness of the tensor-based AAM, we performed variation-robust face recognition using the tensor-based AAM fitting results. To do, we propose indirect AAM feature transformation. Experimental results show that tensor-based AAM with continuous variation estimation outperforms that with discrete variation estimation and conventional AAM in terms of the average fitting error and the face recognition rate.
---
paper_title: Staam: Fitting a 2D+3D AAM to Stereo Images

paper_content:

This paper proposes a Stereo Active Appearance Model fitting algorithm (STAAM), that fits a 2D+ 3D Active Appearance Model to stereo images acquired from calibrated perspective cameras. The STAAM uses geometrical relationship between cameras to compute the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. Also, the STAAM uses a modified simultaneous update fitting method that reduces the fitting computation greatly. Experimental results show that (2) the STAAMshows a better fitting stability than the existing multi-view AAM, (2) the modified simultaneous update algorithm accelerates the AAM fitting speed. 2D+3D Active Appearance Model, Stereo Active Appearance Model.
---
paper_title: Active Appearance Models Revisited

paper_content:

Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimising the error between the input image and the closest model instances i.e. solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed (“projected out”) using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.
---
paper_title: Texture-Constrained Active Shape Models

paper_content:

In this paper, we propose a texture-constrained active shape model (TC-ASM) to localize a face in an image. TC-ASM effectively incorporates not only the shape prior and local appearance around each landmark, but also the global texture constraint over the shape. Therefore, it performs stable to initialization, accurate in shape localization and robust to illumination variation, with low computational cost. Extensive experiments are provided to demonstrate
---
paper_title: Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models

paper_content:

The active appearance models (AAMs) provide the detailed descriptive parameters that are useful for various autonomous face analysis problems. However, they are not suitable for robust face tracking across large pose variation for the following reasons. First, they are suitable for tracking the local movements of facial features within a limited pose variation. Second, they use gradient-based optimization techniques for model fitting and the fitting performance is thus very sensitive to initial model parameters. Third, when their fitting is failed, it is difficult to obtain appropriate model parameters to re-initialize them. To alleviate these problems, we propose to combine the active appearance models and the cylinder head models (CHMs), where the global head motion parameters obtained from the CHMs are used as the cues of the AAM parameters for a good fitting or re-initialization. The good AAM parameters for robust face tracking are computed in the following manner. First, we estimate the global motion parameters by the CHM fitting algorithm. Second, we project the previously fitted 2D shape points onto the 3D cylinder surface inversely. Third, we transform the inversely projected shape points by the estimated global motion parameters. Fourth, we project the transformed 3D points onto the input image and computed the AAM parameters from them. Finally, we treat the computed AAM parameters as the initial parameters for the fitting. Experimental results showed that face tracking combining AAMs and CHMs is more pose robust than that of AAMs in terms of 170% higher tracking rate and the 115% wider pose coverage.
---
paper_title: AAM derived face representations for robust facial action recognition

paper_content:

In this paper, we present results on experiments employing active appearance model (AAM) derived facial representations, for the task of facial action recognition. Experimental results demonstrate the benefit of AAM-derived representations on a spontaneous AU database containing "real-world" variation. Additionally, we explore a number of normalization methods for these representations which increase facial action recognition performance.
---
paper_title: A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

paper_content:

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.
---
paper_title: Light Field Appearance Manifolds

paper_content:

Statistical shape and texture appearance models are powerful image representations, but previously had been restricted to 2D or 3D shapes with smooth surfaces and lambertian reflectance. In this paper we present a novel 3D appearance model using image-based rendering techniques, which can represent complex lighting conditions, structures, and surfaces. We construct a light field manifold capturing the multi-view appearance of an object class and extend the direct search algorithm of Cootes and Taylor to match new light fields or 2D images of an object to a point on this manifold. When matching to a 2D image the reconstructed light field can be used to render unseen views of the object. Our technique differs from previous view-based active appearance models in that model coefficients between views are explicitly linked, and that we do not model any pose variation within the shape model at a single view. It overcomes the limitations of polygonal based appearance models and uses light fields that are acquired in real-time.
---
paper_title: Manifold Estimation in View-based Feature Space for Face Synthesis across Poses

paper_content:

This paper presents a new approach to synthesize face images under different pose changes given a single input image. The approach is based on two observations: 1. a series of face images of a single person under different poses could be mapped to a smooth manifold in the unified feature space. 2. the manifolds from different faces are separated from each other by their dissimilarities. The new manifold estimation is formulated as an energy minimization problem with smoothness constraints. The experiments show that face images under different poses can be robustly synthesized from one input image, even with large pose variations.
---
paper_title: Non-Parametric and Light-Field Deformable Models

paper_content:

Statistical shape-and-texture appearance models use image morphing to define a rich, compact representation of object appearance. They are useful in a variety of applications including object recognition, tracking and segmentation. These techniques, however, have been limited to objects with Lambertian surface reflectance, simple geometry and topology. In this work, we present new shape-and-texture appearance models that overcome these limitations. In the first part of our work, we develop a 4D shape-and-texture appearance model, built using light-fields. This model is capable of representing objects with complex surface reflectance and geometry. We demonstrate our light-field appearance model using 50 light-fields of the human head captured from a real-time camera array. Next, we present a non-parametric appearance model of the shape and texture of objects whose appearance manifolds exhibit a varying topology, e.g., have holes. We demonstrate this model using 2D mouth images of speaking people. In our experiments, we evaluate the performance of each method and provide a comparison with conventional, linear single- and multi-view deformable models.
---
paper_title: An extended multiresolution approach to mouth specific AAM fitting for Speech Recognition

paper_content:

Active Appearance Models (AAMs) are a widely used technique for face tracking. They work by minimising the difference between an unobserved image and a synthetically generated image created by a statistical model of the deformable object, e.g. a face. The Fixed Jacobian algorithm is the most widely used algorithm for fitting AAMs. A Gaussian Image Pyramid fitting technique is used in order to make this process more robust and computationally faster. This paper presents a new image pyramid fitting structure specifically developed for application to an Audio-Visual Speach Recognition (AVSR) system in which the area described by the AAM is reduced as the iterations progress. This allows the fitting technique to be more accurate as the fitting progresses. The new fitting structure is implemented with the Fixed Jacobian algorithm and then compared to a standard approach where the mouth shape is extracted from a full face AAM. The test is performed using images from the CUAVE database. The new structure is shown to be more accurate and robust than the full face approach, with a 14.10% increase in the convergence of the mouth points to within a 4 pixel average difference, while also achieving a 8.53% improvement in the accuracy of the fit.
---
paper_title: Viewpoint-specific active appearance model for robust feature point extraction

paper_content:

Active Appearance Model (AAM) is a popular technique for facial feature point extraction. However, conventional AAM is always viewpoint-generic, which lowers the fitting accuracy. In this paper, a viewpoint-specific AMM is proposed for robust facial point extraction under multi-viewpoint. We investigate three viewpoints, such as 0 degrees, 30 degrees, 60 degrees. The experimental results show a briefly results. By the proposed method, the actuary of the facial point extraction can be largely improved.
---
paper_title: Fast Active Appearance Model Search Using Canonical Correlation Analysis

paper_content:

A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCA-AAMs, while requiring similar implementation effort, consistently outperform standard search with regard to convergence speed by a factor of four
---
paper_title: A Unified Gradient-Based Approach for Combining ASM into AAM

paper_content:

Active Appearance Model (AAM) framework is a very useful method that can fit the shape and appearance model to the input image for various image analysis and synthesis problems. However, since the goal of the AAM fitting algorithm is to minimize the residual error between the model appearance and the input image, it often fails to accurately converge to the landmark points of the input image. To alleviate this weakness, we have combined Active Shape Models (ASM) into AAMs, in which ASMs try to find correct landmark points using the local profile model. Since the original objective function of the ASM search is not appropriate for combining these methods, we derive a gradient based iterative method by modifying the objective function of the ASM search. Then, we propose a new fitting method that combines the objective functions of both ASM and AAM into a single objective function in a gradient based optimization framework. Experimental results show that the proposed fitting method reduces the average fitting error when compared with existing fitting methods such as ASM, AAM, and Texture Constrained-ASM (TC-ASM) and improves the performance of facial expression recognition significantly.
---
paper_title: Fitting a Single Active Appearance Model Simultaneously to Multiple Images

paper_content:

Active Appearance Models (AAMs) are a well studied 2D deformable model. One recently proposed extension of AAMs to multiple images is the CoupledView AAM. Coupled-View AAMs model the 2D shape and appearance of a face in two or more views simultaneously. The major limitation of CoupledView AAMs, however, is that they are specific to a particular set of cameras, both in geometry and the photometric responses. In this paper, we describe how a single AAM can be fit to multiple images, captured simultaneously by cameras with arbitrary geometry and response functions. Our algorithm retains the major benefits of Coupled-View AAMs: the integration of information from multiple images into a single model, and improved fitting robustness.
---

Title: A Review Article for Active Appearance Model Fitting Based on Optimization Methods

Section 1: INTRODUCTION
Description 1: Introduce the Active Appearance Model (AAM), its applications, and provide an overview of the main focus of the paper, which is the critical review of AAM fitting based on optimization methods.

Section 2: ACTIVE APPEARANCE MODEL THEORETICAL BACKGROUND
Description 2: Explain the theoretical foundation of AAM, including the construction phases of the statistical shape model, texture model, and combined appearance model.

Section 3: AAM Fitting based on optimization methods
Description 3: Discuss various approaches to optimize AAM fitting, categorizing these techniques into four sections: manifold deformable models, combining current models, employing specific AAMs, and introducing new fitting algorithms.

Section 4: Manifold deformable model
Description 4: Describe the first optimization category, which utilizes the manifold deformable model to address AAM fitting challenges.

Section 5: Combining the current models
Description 5: Explain the second category involving the combination of existing models like ASMs and AAMs to improve fitting efficiency.

Section 6: Employ specific AAM
Description 6: Detail the third category that involves using specific AAMs tailored for individuals and external variations to enhance fitting precision.

Section 7: Introducing new fitting algorithm
Description 7: Discuss the fourth category where new fitting algorithms are introduced or current ones are enhanced to improve the AAM fitting process.

Section 8: CONCLUSION
Description 8: Summarize the findings and critical review of the fitting optimization techniques for AAM, emphasizing the potential impact of introducing new fitting algorithms on the model's performance.