Learning-Based Approach to Estimation of Mor- phable Model Parameters

Motivation: Amongst the many model-based approaches to modeling object classes, the Linear Morphable Model is an important one (Vetter and Poggio [6], Jones and Poggio [2]). It has been been used successfully to model faces, cars and digits. In these applications, the task of matching a novel image to the LMM is achieved through a computationally intensive analysis by synthesis approach. In Jones and Poggio [2], the matching parameters are computed by minimizing the squared error between the novel image and the model image using a stochastic gradient descent algorithm. This technique may take several minutes for matching even a single image. A technique that could compute the matching parameters with considerably less computations and using only view-based representations would make these models useful in real-time applications.