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3D images provide several advantages over 2D images for face recognition, especially when considering expression variations. In this paper, a novel framework is proposes for 3D-based face recognition. The key idea in the proposed algorithm is a representation of the facial surface, by what is called a Bending Invariant (BI), invariant to isometric deformations resulting from expressions and postures...
This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on...
This paper proposes a learning based framework for efficient 3D face reconstruction. We transfer the 3D reconstruction into a statistical learning problem of finding appropriate mapping between texture and depth subspaces. Instead of using grayscales to directly estimate the depth, we use local binary pattern (LBP) to further encode the face texture, providing robustness for depth estimation under...
This paper uses a recently introduced manifold kernel regression technique to explore the relationship between facial shape and attractiveness on a heterogeneous dataset of over three thousand images gathered from the Web. Using the concept of the Frechet mean of images under a diffeomorphic transformation model, we evolve the average face as a function of attractiveness ratings. Examining these averages...
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