A combined feature-texture similarity measure for face alignment under varying pose

We formulate face alignment as a model-based parameter estimation problem in this paper. First, we work within a framework that combines two separate subspace models to represent frontal face patterns and pose change independently. The combined unified nonlinear model represents varying pose faces with a complex manifold. Then, we use a feature based similarity measure (FBSM) to evaluate image differences in terms of pose, and match unknown pose faces with the model image using a combined feature-feature similarity measure (FTSM). Noticeable properties of the combined FTSM include (1) its sensitivity to spatial differences between feature points in two images, which is crucial to aligning two initially faraway poses; (2) easy determination of hill-climb directions in parameter space, without computing gradients of error functions. Experimental results demonstrate that, in the absence of significant clutter, a face alignment algorithm using the combined FTSM, can reliably align varying pose faces under different lighting conditions, even when initial poses are far off