- Research Article
- Open Access
Adapted Active Appearance Models
© Renaud Séguier et al. 2009
- Received: 5 January 2009
- Accepted: 20 October 2009
- Published: 13 December 2009
Active Appearance Models (AAMs) are able to align efficiently known faces under duress, when face pose and illumination are controlled. We propose Adapted Active Appearance Models to align unknown faces in unknown poses and illuminations. Our proposal is based on the one hand on a specific transformation of the active model texture in an oriented map, which changes the AAM normalization process; on the other hand on the research made in a set of different precomputed models related to the most adapted AAM for an unknown face. Tests on public and private databases show the interest of our approach. It becomes possible to align unknown faces in real-time situations, in which light and pose are not controlled.
- Learning Base
- Illumination Variation
- Active Appearance Model
- Cost Overrun
- Unknown Face
All applications related to face analysis and synthesis (Man-Machine Interaction, compression in video communication, augmented reality) need to detect and then to align the user's face. This latest process consists in the precise localization of the eyes, nose, and mouth gravity center. Face detection can now be realized in real time and in a rather efficient manner [1, 2]; the technical bottleneck lies now in the face alignment when it is done in real conditions, which is precisely the object of this paper.
Since such Active Appearance Models (AAMs) as those described in  exist, it is therefore possible to align faces in real time. The AAMs exploit a set of face examples in order to extract a statistical model. To align an unknown face in new image, the models parameters must be tuned, in order to match the analyzed face features in the best possible way. There is no difficulty to align a face featuring the same characteristics (same morphology, illumination, and pose) as those constituting the example data set. Unfortunately, AAMs are less outstanding when illumination, pose, and face type changes. We suggest in this paper a robust Active Appearance Model allowing a real-time implementation. In the next section, we will survey the different techniques, which aim to increase the AAM robustness. We will see that none of them address at the same time the three types of robustness, we are interested in pose, illumination, and identity. It must be pointed out that we do not consider the robustness against occlusion as  does, for example, when a person moves his hand around the face.
After a quick introduction of the Active Appearance Models and their limitations (Section 3), we will present our two main contributions in Section 4.1 in order to improve AAM robustness in illumination, pose, and identity. Experiments will be conducted and discussed in Section 5 before drawing a conclusion, suggesting new research directions in the last section.
Invariant features (illumination)
Canonical representation (illumination)
- (ii)Parameter space extension
Light modeling (illumination)
3D modeling (pose)
- (iii)Models number increasing
Supervised classification (pose/expression)
Unsupervised classification (pose/expression)
- (iv)Learning base specialization
Hierarchical approach (pose/expression)
Identity specification (identity)
Preprocess methods seek to substitute the AAM texture input for a preprocessed image, in order to minimize the influence of illumination. In Invariant features, an image feature invariant, or a less illumination sensitive variation, is used: an image gradient , specific face features like corner detectors for the eyes and mouth , the concatenation of several colors components (H and S from HSV code and image gradient for example) , wavelet networks , or distance map . Except for the last one, those methods all have a serious drawback: by concatenating the different invariant characteristics, they increase the texture size and therefore the algorithm complexity. Steerable filters  can be used to replace texture information and to characterize the region around each landmarks. The evaluation of those filters increases the algorithm complexity but the amount of information to be process by the AAM remains the same if low resolution models (64 64) are used for real-time application. For high resolution models, a wedgelet representation is proposed  to compress the texture. In a Canonical representation, the illumination variations are normalized  or reduced . The shadows also can be evaluated , in order to recover the face 3D model, and then reproduce a texture without any shadow. Those approaches remain uncertain.
Parameter Space Extension methods increase the number of AAM parameters, in order to model the variability introduced in the learning base, which was used to create the face model. In Light modeling, a subspace in the parameter space is learned and built, in order to control the illumination variation. A modeling throughout the Illumination Cone [15, 16] or Light Fields [17, 18] is suggested. The illumination direction can also be estimated through the construction of a learning base of faces, which were acquired under a number of different illuminations, each of them being created by the variation of a single light source position . The illumination variations are then modeled by the principal component analysis embedded in the AAM. All of those methods make the algorithm cumbersome, since the number of parameters needing optimization is increased, and the parameter space is broken up. The optimization, carried on a bigger and noncompact space parameter, is then more difficult to control. In 3D modeling, the face pose variability is transferred from the appearance parameter space to the sub-space which controls the pose (face position and angle). Reference  introduces a new parameter to be optimized, using the pose information associated to each face represented in the learning base. A 3D AAM can also be used either from the shapes and textures acquired from a scanner , or with a frontal and profile face view of each of the learning base face [22–24]. Reference  enriches the 3D AAM's parameters by using the Candide model parameters related to Action Units to deform the mouth and eyebrows. The 3D approach is clearly relevant to increase the AAM robustness related to the pose variability. Nevertheless, as the 3D model becomes more complex, a real-time implementation remains difficult.
Models number increasing methods specify the classes existing in the parameter space of the AAM parameters and define a specific active model in each of those classes. In Supervised classification, the variability type of the learning base is defined and the classes which make up the parameter space are known: the different face views used for the pose variability [26–29] or the different expressions for the expression variability . A huge model containing each submodel specific to each view can be constructed  by concatenating each shape and texture vectors for each view on two large shape and texture vectors. In Unsupervised classification, the classes which constitute the parameter space are found automatically via K-means  or a Gaussian mixture [33, 34]. For each of these methods, active models are numerous. They must be optimized in parallel, in order to decide which one is best suited for the analyzed face. This is not feasible in real time, in our applicative context. One single model can be used in conjunction with Gaussian mixture  to avoid implausible solution during the AAM convergence.
Learning base specialization methods restrict the search space to only one variability (of one face feature or identity). In Hierarchical approach, face features research is divided in two steps: a rough research of face key points and then a refined analysis of face feature by the mean of a specific model for each face feature (eyes, nose, mouth) [36–39]. Like the previous methods, those approaches consist in increasing the number of active models to be optimized in parallel, and then make the alignment system cumbersome. In Identity specification, the database identity variability is removed. Reference  claims that a generic AAM featuring pose, identity, illumination, and expression variability is less efficient than an AAM dedicated to one identity featuring only pose, illumination, and expression variability. Reference  suggests to perform an on-line identity adaptation on an image sequence, by means of a 3D AAM construction, starting from the first image of the face without any expression. This method is not robust since the first image must be perfectly aligned to allow a good 3D AAM modeling.
None of those methods fulfill our constraints, since none of them take into account unknown faces in variable pose and illuminations, at the same time. Let us recall that our main objective is to keep the AAM real-time aspect, while increasing their robustness. Therefore, we started with Invariant features methods related to illumination robustness, in which the AAM texture is pre-processed, and then later suggested a technique (Section 4.1), which does not increase the AAM computation cost. With regard to the robustness associated with pose and identity, and considering the work presented in Identity specification as a start point, we propose to adapt the active model to the analyzed person by means of precomputed AAMs (Section 4.2).
and are the mean shape and mean texture, and are both vectors representing the variations of the orthogonal modes related to shape and texture, respectively. and are both vectors representing shape and texture parameters. We then apply a third PCA on vectors .
is the matrix of the eigenvectors obtained by PCA. is the appearance parameters vector. To each eigenvector is associated an eigenvalue, which indicates the amount of deformation it can generate. In order to reduce the vector dimension, we keep of the model deformation. It is then possible to synthesize an image of the object with the appearance vector .
Our two main contributions consist of the Oriented Map Active Appearance (OMAP) Models to give AAM the capacity to align the face in any illumination conditions; the Adapted AAM for pose and identity robustness.
4.1. OM-AAM: Oriented Map Active Appearance Models
A comparison  between the Viola and Jones face detector  and Froba's one  shows that their relative performances are equivalent when the background is uniform. The first detector is more efficient when faced with a complex background, but is also more difficult to implement. In our application, faces are previously detected and we must align them. The background does not disturb very much the AAM performances.
As we can see in Figure 2, after the mapping process, the edges close to the vertical (orientation angle close to zero, or ) will get a low level of information on an oriented map and those, close to an horizontal position (orientation angle close to or ), will produce a high level of information.
In the segmentation phase, we evaluate the difference between the texture synthesized thanks to the model and the texture analyzed in the image (Figure 3(f)). This texture, in classical AAM, is normalized in luminance and shape at each iteration. The photometric normalization is no longer necessary in our case, since the new texture results in an angle evaluation. When the object is oriented with an angle of , we shift the model with respect to the vector (3) and evaluate a difference between the original image inside the model obtained shape, and the model obtained texture. The difference between those two textures is made in the reference model: a normalized shape with an orientation .
We can see in Figure 4(c) that this operation allows the comparison of the orientation information lying in the model texture and the analyzed image texture, whatever the object orientation is.
In order to be able to subtract the offset (14), we need to keep the original values of the edge angle, detected in the image. Therefore, we propose to evaluate, during the segmentation phase, the oriented map between and in the pre-process step (Algorithm 2 ( ).(b)), and to realize at each iteration, during the optimization phase, the mapping (Algorithm 2 ( ).(c).(ii)) and the product (Algorithm 2 ( ).(c).(iii)) operated by the nonlinear function . This function is evaluated during the pre-process (Algorithm 2 ( ).(c)) and is, then, not time consuming. This new segmentation proposition is summarized by the following Algorithm 2.
- (3)Optimization. Repeat (a) to (e)
Tune the model parameters.
The cost overrun generated by the oriented map is in the order of operations. In real context, we use a texture of pixels and a shape of key points for an appearance vector comprising approximately parameters (see (8)). The optimization cost overrun is , bearing in mind that the warping consumes fifty percent of the process time. In our implementation, we effectively observe a similar increase ( to be precise) when we compare the process time related to the classical AAM, and the one related to our proposition, pre-process step included (Algorithm 2 ( )).
As previously said in Section 3.3, the AAM robustness is related to the face variability in the learning base. A great variability induces a multi-manifold parameter space which disturbs the AAM convergence. Instead of using a very generic model containing a lot of variability, we suggest to use an initial model , which contains only a variability in identity, and then use a specific model , containing variability in pose and expression.
4.2.1. Initial Model
4.2.2. Type Identification of the Analyzed Face
4.2.3. Adapted Model
From , we generate the adapted model . When , 2, or 3, it is possible to evaluate beforehand the adapted model, depending on the number of different faces in the general database. For this database can contain up to one hundred faces, since the total number of combinations is around five thousands, and GB will then be sufficient to store the five thousand models. If then comparatively small general database will be used, that is, 33 different faces if only GB memory is available in the system.
When we need to align an unknown face in a static image, we then simply align the face with the initial model and apply the pre-computed model, which corresponds to the nearest faces. If a video stream related to one person needs to be analyzed, we use the first second of the stream in order to perform a more robust selection of the adapted model. On the first images, we align the face with the initial model . We evaluate the error (7) on each image. This error is remarkably stable, because of the use we make of the oriented map; it is then possible to compare it to a threshold, in order to decide if the model has converged. We then evaluate, from the correctly aligned faces, the nearest identities which must be taken into account in the general database, in order to construct the adapted model. This model is then used on the following images in the video stream, in order to align the face.
We will specify hereafter the parameters values and metric to evaluate the performances of our two contributions (OM-AAM and Adapted AAM). This section will end with a discussion on the different results.
5.1. Experiments Setup
where is the error made on one of the points representing the eyes, nose, and mouth centers; is the distance between the eyes. In the context of the robustness analysis to illumination, identity, and pose, those four points are sufficient to illustrate the performances of our proposals. The precision of the ground truth is roughly of the distance between the eyes of the annotated faces; beyond , we consider that the alignment is not correct. We will then evaluate the error in the range .
A texture of 1756 pixels is used, in association with a 68-key points shape model and we keep of the deformation, in order to reduce the appearance vector dimension. With regard to the oriented map, no specific parameterization is necessary: the orientation number ( ) is quantified on height bits and is not related to the type of the testing base images.
5.2. OM-AAM Performances
Let us remember that our objective is to make the AAM robust to illumination variations without any increase in the processing time. The DM-AAM of  complies with our constraints. We then propose to illustrate the OM-AAM performances, in comparison to those of the DM-AAM and classical AAM. Those comparisons will be made in a generalization context: the faces used to construct the model (18 persons from the M2VTS database ) and the ones used for the tests come from distinct databases.
A reference point used in the state of the art technology is often the point of abscissa . On the CMU-PIE database, OM-AAMs are able to align of the faces with a precision less or equal to , when DM-AAM and classical ones are less efficient: their performances are, respectively, and . But when the faces are acquired in real situations, our proposition overcomes other methods: in the BIOID database, OM-AAM can align of the faces with a precision less or equal to , which represents a and performance gain, with regard to classical AAM and DM performances, respectively.
5.3. Adapted AAM Performances
We propose to test the adapted AAM on the static images of the general database (Figure 5). A test sequence is then made, with one unknown person presenting four expressions under five different poses; the learning base associated to this testing base is made of all the other persons. A cross-validation of type Leave-one-out is used. All faces are tested separately, using all the other ones for the learning base. All the faces of the database have been tested, representing at the end a set of 580 images with a big variety of poses, expressions, and identity. The initial database used to generate the initial model is the same as the one presented in Figure 6, apart from the fact that the testing face has been removed. It contains then 28 different faces. This model is applied on every single 20 images of the unknown face, in order to evaluate the nearest faces. Then the adapted model is finally applied on those 20 images in order to align them (detect the gravity center of the eyes, nose, and mouth). In order to analyze separately the benefits of the proposed algorithm, we use only classical normalized textures instead of oriented ones.
We compare the performances of our system when (Adapted AAM) to those of three others different AAM. The first one (AAM 28) gets identity as the only variability and is made of the 28 faces (the twenty-ninth being tested) in frontal view and neutral expression. The second one (AAM 560) is full of rich variability, since it is based on 560 images representing 28 faces, representing themselves four expressions under five different poses. Lastly the third one (AAM GM)  (see Section 2) uses Gaussian mixtures to specify the regions of plausible solutions in the parameter space (see Figure 13). It is interesting to compare our proposition to this method since it is dedicated to multi-manifold spaces. We cannot implement it on a restricted database like the one of "AAM 28" which represents only one cluster of frontal faces. Four Gaussians were used to catch the density on the 560 images of the rich database of "AAM 560" model. We use the three first components of the appearance vector as it was indicated by the authors since the density in the other dimensions is uniform.
5.4. Adapted AAM Performances Discussion
The algorithmic complexity of "Adapted AAM" and "AAM 28" is almost the same, since their appearance vector dimension is similar (around 25). Conversely, "AAM 560" and "AAM GM" are much more complex (appearance vector dimension around 250) and exclude a real-time implementation. As it was said in Section 3.2 the warping takes of total processing time for real-time implementation when dimension of parameter vector is less than 30 and small textures are used like the ones we implement in this paper. To be precise, the ten iterations used to align a face takes 9.3 ms on a P4-2GHz. Usually for real implementation, we test the AAM on three different scales and nine positions around the detected center of the face, so we need 251 ms to align the face. The results presented here use those different scales and positions. After one second we switch to tracking mode: only five positions are tested around the center of the face so the algorithm works at 21 Hz. If the dimension of the appearance vector (see (8)) is multiplies by ten, then the number of operations is rougthly multiplied by ten too, with the warping time being not affected by this dimension growth. Even in tracking mode, this increase will then lead to only a 2 Hz framerates for "AAM 560" or "AAM GM" which is not sufficient for real time applications.
If we look at the reference error ( ), then our proposition is ten times more rapid than the "AAM 560" because of the dimension of the appearence vector, and clearly more effective (performances improvment of ) than the same heavy "AAM 560" model. If we compare now the "Adapted AAM" to the other light model (AAM 28), the "Adapted AAM" has the same complexity and is more effective for of the images of the testing base. As a conclusion, our model is more rapid and effective than other models, because it has focused on a relevant database, which is related to the testing face.
Active Appearance Models are very efficient to align known faces in constraints conditions (face pose and illumination). In order to make them robust to illumination variations, we have proposed a new AAM texture type and a new normalization during the optimization step. In order to make them robust to unknown faces moving in unknown poses in different expressions, we have suggested an adapted model. This adaptation is made by choosing, in a set of pre-computed models, the best suited model to the unknown face. Tests made on public and private databases have shown the interest of our propositions; it is now possible to align unknown faces in nonconstraint situations, with a precision, which is sufficient enough for most applications requiring an alignment process (face recognition, face gesture analysis, cloning). Unlike  (cf. Section 2), where a specific model is made out of the first image of a video stream, we seek for the model which is best suited to the unknown face. This difference is significant; an imperfect initial alignment has no definitive repercussions. Our system is then more robust in view of the errors made by the initial generic model. At last, it is to be noted that the Adapted-AAM with oriented texture offers the same computational complexity as the classical AAM; they can be implemented in real time.
For emotion analysis and lip-reading, it is necessary to have a very precise alignment in order to be able to track the face dynamic. Precisely, the alignment performances must be evaluated on the localization of several points around the eyes, eyebrows, and mouth and not only on their gravity centers. We are now working on an adapted and hierarchical AAM, which use for each face characteristics (eyes and mouth essentially), the most relevant adapted model.
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