Blur-Robust Face Recognition via Transformation Learning

Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred im-ages to a same feature subspace by the method of multidimensional s-caling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the mani-fold structure of the original shape images and, at the same time, en-hances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of un-known blur degree, by applying the feature transformation correspond-ing to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance a-gainst the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

An integrated Bayesian approach to shape representation and perceptual organization

Abstract We present a unified Bayesian approach to shape representation and related problems in perceptual organization, including part decomposition, shape similarity, figure/ground estimation, and 3D shape. The approach is based on the idea of estimating the skeletal structure most likely to have generated the observed shape via a process of stochastic “growth. ” We survey the approach briefly and show how it can be extended in a principled way to solve a wide array of related problems. 1 Shape and perceptual organization The visual representation of shape is a complex problem, requiring the reduction of an essentially infinite-dimensional object (the geometry of the shape) to a few perceptually meaningful dimensions. Human infants can recognize shape from line form from the bounding contour is innate. Much research in the study of shape has involved a quest for a set of shape descriptors that will allow just the right aspects of shape to be extracted—a representation that retains enough information to support recognition, shape similarity, and other key functions. Each of these techniques— geons [3], codons [37], medial axes [4], curvature extrema [18], Fourier descriptors [8], and so forth—has merits. Some have compelling mathematical motivations, while others (unfortunately not usually the same ones) have demonstrable agreement with human data. Still, broadly speaking, a complete computational characterization of human shape representation remains elusive