To obtain the model image, we must morph the reference image according to the model parameters. This produces an image against which we can compare the novel input image. Given this error function we find the best matching model image by optimizing the error with respect to the model parameters using a stochastic gradient descent algorithm. To illustrate our model and matching algorithm, figure 1 shows a few prototypes from a database of frontal views of faces. Figure 2 shows a few novel faces which were not used as prototypes and the resulting best match after fitting the model.
Top row has novel faces that the algorithm attempted to match with the model.
Bottom row contains the resulting best fitting model image.