G. J. Edwards, A. Lanitis, C. J. Taylor, and T. F. Cootes. Modelling the variability in face images. 2nd Face and Gesture, pages 328--333, 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a mean shape avoids the need to compute many correspondence maps. However, matching still entails a relatively expensive search over parameters of variations in shape and texture. The match can be based on models of the local appearance of the landmarks [38] or along curves joining the landmarks [14,22,25]. Crucially, linear active shape models are based on a number of implicit as sumptions: a) the shape of the object of interest can be defined by a relatively small set of explicit view models, b) the grey levels around a particular landmark are consistent for all the views of the object and ....

G. Edwards, A. Lanitis, C. Taylor, and T. Cootes. Modelling the variability in face images. In IEEE Face 4 Gesture Recognition, pages 328-333, Killington, Vermont7 1996.

....faces were developed for face recognition and image coding [4, 18, 37] Different approaches have been taken to automate the matching step necessary for building up morphable models. One class of techniques is based on optic flow algorithms [5, 4] and another on an active model matching strategy [12, 16]. Combinations of both techniques have been applied to the problem of image matching [36] In this paper we extend this approach to the problem of matching 3D faces. The correspondence problem between different threedimensional face data has been addressed previously by Lee et al. 20] Their ....

G.J. Edwards, A. Lanitis, C.J. Taylor, and T.F. Cootes. Modelling the variability in face images. In Proc. of the 2nd Int. Conf. on Automatic Face and Gesture Recognition, IEEE Comp. Soc. Press, Los Alamitos, CA, 1996.

....Also the coarse scales are less susceptible to image noise. The finer scale features could be used to locally refine an approximate match made by a coarse scale salient point, but this is beyond the scope of this paper. 6 Results We attempted to locate 20 salient points from a mean face [6,10], in 188 face images of different people. A mean face was used so to ensure that all salient points represent typical features which exist in all faces and not unusual features such as 13 points on glasses etc. We recorded if the correct match for each salient point was the most similar point, in ....

G. Edwards. Modelling the Variability in Face Images. In International Workshop on Automatic Face and Gesture Recognition 1996, pages 328--333, Killington, USA, 1996.

....be salient (black indicates a fine scale and white a coarse scale) The troughs of the saliency image are plotted on images (a) and (b) as circles. The size of the circle represents the scale at which the point is salient. 6 Results We attempted to locate 20 salient points from a mean face [6] [10] in 188 face images of different people. A mean face was used so to ensure that all salient points represent typical features which exist in all faces and not unusual features such as points on glasses etc. We recorded if the correct match for each salient point was the most similar point, ....

G. Edwards. Modelling the Variability in Face Images. In International Workshop on Automatic Face and Gesture Recognition 1996, pages 328--333, Killington, USA, 1996.

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G. J. Edwards, A. Lanitis, C. J. Taylor, and T. F. Cootes. Modelling the variability in face images. 2nd Face and Gesture, pages 328--333, 1996.

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G. J. Edwards, A. Lanitis, C. J. Taylor, and T. F. Cootes. Modelling the variability in face images. 2nd Face and Gesture, pages 328--333, 1996.

....a smaller space, improving identity recognition. 2 Background Facial coding requires the approximation of a manifold, or high dimensional surface, on which any face can be said to lie. This allows accurate coding, recognition and reproduction of previously unseen examples. Previous studies [3, 4, 5] have suggested that using a shape free coding provides a ready means of doing this, at least the when the range of pose angle is relatively small, perhaps Sigma20 o [6] Here, the correspondence problem between faces is first solved by finding a pre selected set of distinctive points (corners ....

.... i (2) between faces q 1 and q 2 , coding in terms of expected variation [7] Redundancies between shape and grey levels are removed by performing separate PCAs upon the shape and grey levels, before the weights of the ensemble are combined to form single vectors on which a second PCA is performed [4]. This appearance model allows the description of the face in terms of true variation the distortions needed to move from one to another. However, it will code the entire space as specified by our set of images, as can be seen in Figure 1. Thus, for example, the distance between the ....

[Article contains additional citation context not shown here]

G. J. Edwards, A. Lanitis, C. J. Taylor and T. F. Cootes. Modelling the variability in face images. 2nd Face and Gesture, pages 328--333, 1996.

....the initial spaces, improving identity recognition. 2 Background Facial coding requires the approximation of a manifold, or high dimensional surface, on which any face can be said to lie. This allows accurate coding, recognition and reproduction of previously unseen examples. Previous studies [2, 3, 4] have suggested that using a shape free coding provides a ready means of doing this, at least the when the range of pose angle is relatively small, perhaps Sigma20 o [5] Here, the correspondence problem between faces is first solved by finding a pre selected set of distinctive points (corners ....

.... i (2) between faces q 1 and q 2 , coding in terms of expected variation [6] Redundancies between shape and grey levels are removed by performing separate PCAs upon the shape and grey levels, before the weights of the ensemble are combined to form single vectors on which a second PCA is performed [3]. This appearance model allows the description of the face in terms of true variation the distortions needed to move from one to another. However, it will code the entire space as specified by our set of images, as can be seen in Figure 1. Thus, for example, the distance between the ....

[Article contains additional citation context not shown here]

G. J. Edwards, A. Lanitis, C. J. Taylor and T. F. Cootes. Modelling the variability in face images. 2nd Face and Gesture, pages 328--333, 1996.

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