G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. AFG, 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....more information about the visual objects be acquired, but also the temporal continuity and subject constancy can provide a more robust representation [8] Gong et al. 9] introduced an approach that uses Partially Recurrent Neural Networks to recognise temporal signatures of faces. Edwards et al. [6] proposed an integrated approach to decouple the identity variance from the residual variance of pose, lighting and expression. By learning the correlation between the two parts of variance online, a class specific refinement for the identity covariance can be achieved. Yamaguchi et al. 19] ....

G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. In IEEE International Conference on Automatic Face & Gesture Recognition, pages 300--305, Nara, Japan, 1998.

....we demonstrate our head model and in Section 6 provide concluding remarks and discuss future work. 2. Previous Work Statistical models based on linear manifolds of shape and texture variation have been widely applied to the modeling, tracking, and recognition of objects with sets of 2D features [4, 11, 14]. In these methods small amounts of pose change are typically modeled implicitly as part of shape variation on the linear manifold. For representing objects with large amounts of rotation, nonlinear models have been proposed, but are complex to optimize [18] Figure 1: Light field camera array ....

G. Edwards, C. Taylor, and T. Cootes, "Interpreting face images using active appearance models," in 3rd International Conference on Automatic Face and Gesture Recognition, 1998, pp. 300305.

....The key idea to all of these generative models is to perform a per pixel comparison between unseen input images and synthesised images and subsequently drive these to equality. In this paper, we investigate a generative model that has proven widely applicable. The Active Appearance Models (AAMs) [12], 13] have been applied to most of the examples given above. As Cootes et al. 14] the appearance of edge strength is modelled, but in contrast this is augmented with colour information and conventional raw intensities. We show that a considerable gain in accuracy can be achieved, merely by ....

....a more appropriate representation of the particular object class being modelled. As such, this paper demonstrates that mature image processing methods can co exist in rewarding symbiosis with a modern generative model based vision technique. II. Active Appearance Models Active Appearance Models [12], 13] establish a compact parameterisation of object variability, as learned from a training set by estimating a set of latent variables. The modelled object properties are usually shape and pixel intensities. The latter is henceforward denoted texture. From these quantities new images similar to ....

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G.J. Edwards, C. J. Taylor, and T. F. Cootes, \Interpreting face images using active appearance models," in Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition. 1998, pp. 300-5, IEEE Comput. Soc.

....for real time performance, followed by our conclusions in Section 5. 2. FACE AND FACIAL FEATURE TRACKING USING AN ACTIVE MODEL This section briefly describes how our active model works. For more details, see [6] 7] The concept of active appearance models (AAMs) was introduced a few years ago [8], and has been the subject of several reports and investigations [9] 13] especially by the original inventors. Together with the AAMs came a directed search algorithm for adapting the model to an image. The algorithm can be used on a complete appearance model, or, as here, on a simpler model ....

G.J. Edwards, T.F. Cootes, and C.J. Taylor, "Inter- preting Face Images using Active Appearance Models," Proc. 3rd Int. Conf. on Automatic Face and Gesture Recognition, pp. 300-305, Nara, Japan, 1998.

....more information about the visual objects be acquired, but also the temporal continuity and subject constancy can provide a more robust representation [8] Gong et al. 9] introduced an approach that uses Partially Recurrent Neural Networks to recognise temporal signatures of faces. Edwards et al. [6] proposed an integrated approach to decouple the identity variance from the residual variance of pose, lighting and expression. By learning the correlation between the two parts of variance online, a class specific refinement for the identity covariance can be achieved. Yamaguchi et al. 19] ....

G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. In IEEE International Conference on Automatic Face & Gesture Recognition, pages 300--305, Nara, Japan, 1998.

.... approach uses similarity vectors to estimate head pose and recognise faces across views [10] Active Shape Model (ASM) and Active Appearance Model (AAM) capturing both shape and shape free grey level appearance of face images have been successfully applied to face modelling and recognition [3, 4, 7]. Both ASM and AAM have been extended to nonlinear cases across views based on Kernel Principal Component Analysis (KPCA) 17, 19, 18] These nonlinear models aimed at corresponding dynamic appearances of both shape and texture across views. In most of the previous work, the basic methodology ....

G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. In IEEE International Conference on Automatic Face & Gesture Recognition, pages 300--305, Nara, Japan, 1998.

.... approach uses similarity vectors to estimate head pose and recognise faces across views [8] The Active Shape Model (ASM) and Active Appearance Model (AAM) capturing both shape and shape free grey level appearance of face images have been successfully applied to face modelling and recognition [2, 3, 5]. Both ASM and AAM have been extended to nonlinear cases across views based on Kernel Principal Component Analysis (KPCA) 14, 16, 15] These nonlinear models are designed to correspond dynamic appearances of both shape and texture across views. In most of the previous work, the basic methodology ....

G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. In IEEE International Conference on Automatic Face & Gesture Recognition, pages 300--305, Nara, Japan, 1998.

....not. One criticism of these approaches is that they attempt to model the entire image as a single rigid patch making it difficult to model changes in pose and feature location. More recently these technique have been generalized to include schemes for modelling deformations in the image plane [1, 8, 5, 10]. These techniques not only learn a set of allowed variations in the image values, but also a set of allowed variations in pixel location. Nevertheless, reliable detection and recognition of faces across a wide variety of faces is not yet a solved problem. More recently a number of more general ....

G.J. Edwards, C.J. Taylor and T.F. Cootes, "Interpreting Face Images using Active Appearance Models" in Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition, IEEE, 1998, pp. 300-305.

.... of these approaches, however, is that they attempt to model the entire image as a single rigid patch making it dicult to model changes in pose and feature location [22, 21, 15] More recently, these technique have been generalized to include schemes for modelling deformations in the image plane [1, 9, 6, 11]. These techniques not only learn a set of allowed variations in the image values, but also a set of allowed variations in pixel location. Nevertheless reliable detection and recognition of images across a wide variety of images is not yet a solved problem. A number of more general statistical ....

G.J. Edwards, C.J. Taylor and T.F. Cootes, \Interpreting Face Images using Active Appearance Models" in Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition, IEEE, 1998, pp. 300-305.

....into the original blending weights. We do not allow extrapolation outside of the range [ 0:5: 1:5] to avoid generating unrealistic face blends. 5.2.2 Optimization procedure Fitting a linear combination of faces to an image has already been explore by different research groups. Edwards et al. [21] developed a model built out of registered face images. In their work, a principal component analysis is applied to the set of faces to extract a set of parameters. The dependencies between the parameters and the images generated by the model are approximated by a linear function. This ....

G.J. Edwards, C.J. Taylor, and T.F. Cootes. Interpreting face images using active appearance models. In Proceedings, Third Workshop on Face and Gesture Recognition, pages 300--305, 1998.

....basic expression. These renderings I k , are then blended together using the weights w k to produce the final image I: I = X k w k I k (2) 2.2 Optimization procedure Fitting a linear combination of faces to an image has already been explored by different research groups. Edwards et al. [9] developed a model built out of registered face images. In their work, a principal component analysis is applied to the set of faces to extract a set of parameters. The dependencies between the parameters and the images generated by the model are approximated by a linear function. This ....

G.J. Edwards, C.J. Taylor, and T.F. Cootes. Interpreting face images using active appearance models. In Proceedings, Third Workshop on Face and Gesture Recognition, pages 300--305. 1998.

....the shape of the model instance remains similar to the shapes of the training examples. Active appearance models A limitation of the ASM is that its prior model does not consider gray level variation of the object instance across images. To overcome this difficulty, Edwards, Cootes, and Taylor [84 86] proposed an extension to the ASM, called active appearance models (AAM) In AAM, a new prior model is constructed using both shape and grey level information. Because the objects represented by AAMs are more specific than those represented by ASMs, in many applications, AAMs can lead to more ....

G. J. Edwards, C. J. Taylor, and T. F. Cootes, "Interpreting face images using active appearance models," in Proc. Int'l Conf. Automatic Face Gesture Recog., pp. 300-- 305, 1998.

....are not. One criticism of these approaches is that they attempt to model the entire image as a single rigid patch making it di cult to model changes in pose and feature location. More recently these technique have been generalized to include schemes for modelling deformations in the image plane [1, 8, 5, 10]. These techniques not only learn a set of allowed variations in the image values, but also a set of allowed variations in pixel location. Nevertheless reliable detection and recognition of faces across a wide variety of faces is not yet a solved problem. More recently a number of more general ....

G.J. Edwards, C.J. Taylor and T.F. Cootes, \Interpreting Face Images using Active Appearance Models" in Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition, IEEE, 1998, pp. 300-305.

....recognition and one often avoided. This is essentially because different faces in similar poses appear more alike than the same face at very differing views. Several researchers have shown that the identity information in human faces is obtainable and transferable to novel views. Edwards et al. [2] have demonstrated that a linear discriminant analysis in an eigenspace of face shapes textures can maintain identity information over a range of poses. DuvdevaniBar et al. [1] have shown that a neural network classifier can generalise to novel views of unseen subjects by similarity to a chorus of ....

....Confusingly, there exists a useful and near linear region around the frontal pose ( Sigma20 o ) for which most models seem to work. This restricted pose variation is often considered sufficient to illustrate pose invariance for recognition. Such limitations apply to the studies of Edwards et al. [2] and Wiskott et al. [9] In human face recognition small variations are rarely considered the transfer of information from one viewing condition to another is usually only examined for full, three quarter and profile views. Attempts at such large pose varying recognition have been given by ....

G.J. Edwards, C.J. Taylor and T. Cootes. "Interpreting Face Images using Active Appearance Models." Proc. of the 3rd Int. Conf. on Automatic Face and Gesture Recognition, Nara, Japan, pp 300-305, 1998.

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G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance 300--305, Japan, 1998.

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G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In International Conference on Automatic Face and Gesture Recognition 1998.

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G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 on Automatic Face and Gesture Recognition 1998.

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G.J. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998.

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G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998.

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G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In rd International Conference on Automatic Face and Gesture Recognition 1998.

....done by interpolating between a set of common landmarks placed on all training examples. Figure 2 shows some examples of such landmarks placed on faces. Figure 2: Examples of face images with common landmarks. We then define the features we wish to model at each scale. We calculate mean face [3] based on the training examples. We then model one feature for each pixel in the mean face. The approximate number of pixels in the mean face, # # , can be set according to the computational power available. # # defines the number of features per scale. The total number of features modelled is # # ....

G.J. Edwards, T.F. Cootes, and C.J. Taylor. Interpreting Face Images using Active Appearance Models. In International Workshop on Automatic Face and Gesture Recognition

.... areas, including face recognition [11] industrial inspection [7] and medical image interpretation [6] They have been extended to search 3D images [10] Active Appearance Models were introduced more recently [2] They have proved very successful for interpreting and tracking images of faces [9], and have been applied to medical image interpretation [1] They been extended to model and search colour images [8] 3 Appearance Models An appearance model can represent both the shape and texture variability seen in a training set. The training set consists of labelled images, where key ....

G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition

....the effect of constraints on the performance of model matching. 1. Introduction Statistical models of appearance have been shown to be useful for interpreting images of deformable objects, particularly when combined with a fast matching algorithm such as Active Appearance Model (AAM) search [4]. In many practical applications an AAM alone is insufficient. A suitable initialisation is required for the matching process and when unconstrained the AAM may not always converge to the correct solution. The appearance model provides shape and texture information which are combined to generate a ....

.... appearance which combine shape and texture variation have been described by various groups including Edwards et al. 5] and Jones and Poggio [9] Edwards et al. initially matched the models to new images using an Active Shape Model approach [3] but later developed the Active Appearance Model [4]. Many groups have placed deformable model matching in a statistical framework, for instance [7, 1, 2] Hug et al. 8] describe how constraints can be applied to statistical models of shape. They show how to generate the reduced set of modes of variation obtained when a subset of the model points ....

G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998, pages 300--305, Japan, 1998.

....changes, the parameters, c, trace out an approximately elliptical path. We can learn the relationship between c and head orientation, allowing us to both estimate the orientation of any head and to be able to synthesize a face at any orientation. By using the Active Appearance Model algorithm [22, 12] we can match any of the individual models to a new image rapidly. If we know in advance the approximate pose, we can easily select the most suitable model. If we do not know, we can search with each of the ve models and choose the one which achieves the best match. Once a model is selected and ....

....models. In the following we describe the techniques in more detail and give examples of the model, its ability to estimate pose, to track faces and to synthesize unseen views. 12.1 Related Work Statistical models of shape and texture have been widely used for recognition, tracking and synthesis [39, 47, 22, 70], but have tended to only be used with near fronto parallel images. Moghaddam and Pentland [52] describe using view based eigenface models to represent a wide variety of viewpoints. Our work is similar to this, but by including shape variation (rather than the rigid eigen patches) we require ....

G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998, pages 300-305, Japan, 1998.

....model can be used to synthesize new views given a single view. Though this can perhaps be done most e ectively with a full 3D model [64] we demonstrate that good results can be achieved just with a set of 2D models. The joint model can also be used to constrain an Active Appearance Model search [15, 7], allowing simultaneous matching of frontal and pro le models to pairs of images. 11.1 Training Data To explore the ability of the apperance models to represent the face from a range of angles, we gathered a training set consisting of sequences of individuals rotating their heads through 180 o ....

....that the models can be used to track faces through wide angle changes, and that they can be used to predict appearance from new viewpoints given a single image of a person. 11.8 Related Work Statistical models of shape and texture have been widely used for recognition, tracking and synthesis [31, 39, 15, 62], but have tended to only be used with near fronto parallel images. Moghaddam and Pentland [46] describe using view based eigenface models to represent a wide variety of viewpoints. Our work is similar to this, but by including shape variation (rather than the rigid eigen patches) we require ....

G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998, pages 300-305, Japan, 1998.

....changes, the parameters, c, trace out an approximately elliptical path. We can learn the relationship between c and head orientation, allowing us to both estimate the orientation of any head and to be able to synthesize a face at any orientation. By using the Active Appearance Model algorithm [4, 1] we can match any of the individual models to a new image rapidly. If we know in advance the approximate pose, we can easily select the most suitable model. If we do not know, we can search with each of the ve models and choose the one which achieves the best match. Once a model is selected and ....

....2D models. In the following we describe the techniques in more detail and give examples of the model, its ability to estimate pose, to track faces and to synthesize unseen views. 2 Background Statistical models of shape and texture have been widely used for recognition, tracking and synthesis [7, 9, 4, 14], but have tended to only be used with near fronto parallel images. Moghaddam and Pentland [11] describe using viewbased eigenface models to represent a wide variety of viewpoints. Our work is similar to this, but by including shape variation (rather than the rigid eigenpatches) we require ....

[Article contains additional citation context not shown here]

G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998, pages 300-305, Japan, 1998.

....model can be used to synthesize new views given a single view. Though this can perhaps be done most e ectively with a full 3D model [18] we demonstrate that good results can be achieved just with a set of 2D models. The joint model can also be used to constrain an Active Appearance Model search [3, 1], allowing simultaneous matching of frontal and pro le models to pairs of images. In the following we describe the techniques in more detail and give examples of the models, their ability to synthesize new views and to search unseen images. 2 Background Statistical models of shape and texture ....

....to pairs of images. In the following we describe the techniques in more detail and give examples of the models, their ability to synthesize new views and to search unseen images. 2 Background Statistical models of shape and texture have been widely used for recognition, tracking and synthesis [7, 9, 3, 17], but have tended to only be used with near fronto parallel images. Moghaddam and Pentland [12] describe using view based eigenface models to represent a wide variety of viewpoints. Our work is similar to this, but by including shape variation (rather than the rigid eigen patches) we require ....

[Article contains additional citation context not shown here]

G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998, pages 300-305, Japan, 1998.

.... areas, including face recognition [11] industrial inspection [7] and medical image interpretation [6] They have been extended to search 3D images [10] Active Appearance Models were introduced more recently [2] They have proved very successful for interpreting and tracking images of faces [9], and have been applied to medical image interpretation [1] They been extended to model and search colour images [8] 3 Appearance Models An appearance model can represent both the shape and texture variability seen in a training set. The training set consists of labelled images, where key ....

G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 rd International Conference on Automatic Face and Gesture Recognition 1998, pages 300--305, Japan, 1998.

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G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. AFG, 1998.

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G. J. Edwards, C. J. Taylor, and T. F. Cootes, "Interpreting face images using active appearance models," in Proc. Int'l Conf. Automatic Face Gesture Recog., pp. 300-- 305, 1998.

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Edwards, G., Taylor, C., and Cootes, T. (1998). Interpreting face images using active appearance models. In Int. Conf. on Face and Gesture Recognition, pages 300--305.

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G. Edwards, C. Taylor, and T. Cootes, "Interpreting face images using active appearance models," in 3rd International Conference on Automatic Face and Gesture Recognition, Japan, 1998, p. 300305.

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G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. In Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition, pages 300--305, 1998.

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G. J. Edwards, T. F. Cootes, and C. J. Taylor. Interpreting face images using active appearance models. In Proc. International Conference on Automatic and Gesture Recognition, pages 300--305, Japan, 1998.

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G. J. Edwards, T. F. Cootes, and C. J. Taylor. Interpreting face images using active appearance models. In Proc. International Conference on Automatic and Gesture Recognition, pages 300--305, Japan, 1998.

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G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In 3 ############# ##################### #### ### ####### ########### ####, pages 300-305, Japan, 1998.

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G. Edwards, C. Taylor, and T. Cootes. Interpreting face images using active appearance models. In IEEE International Conference on Automatic Face & Gesture Recognition, pages 300--305, Nara, Japan, 1998.

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G. J. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In Proc. International Conference on Automatic Face and Gesture Recognition, pages 300--305, June 1998.

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Edwards G; Taylor C; Cootes T. Interpreting face images using active appearance models. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998, page(s): 300-305.

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