Derkach D, Sukno FM. Automatic local shape spectrum analysis for 3D facial expression recognition. Image and Vision Computing
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Back Derkach D, Sukno FM. Automatic local shape spectrum analysis for 3D facial expression recognition. Image and Vision Computing
Derkach D, Sukno FM. Automatic local shape spectrum analysis for 3D facial expression recognition. Image and Vision Computing.
We investigate the problem of Facial Expression Recognition (FER) using 3D data. Building from one of the most successful frameworks for facial analysis using exclusively 3D geometry, we extend the analysis from a curve-based representation into a spectral representation, which allows a complete description of the underlying surface that can be further tuned to the desired level of detail. Spectral representations are based on the decomposition of the geometry in its spatial frequency components, much like a Fourier transform, which are related to intrinsic characteristics of the surface. In this work, we propose the use of Graph Laplacian Features (GLFs), which result from the projection of local surface patches into a common basis obtained from the Graph Laplacian eigenspace. We extract patches around facial landmarks and include a state-of-the-art localization algorithm to allow for fully-automatic operation. The proposed approach is tested on the three most popular databases for 3D FER (BU-3DFE, Bosphorus and BU-4DFE) in terms of expression and AU recognition. Our results show that the proposed GLFs consistently outperform the curves-based approach as well as the most popular alternative for spectral representation, Shape-DNA, which is based on the Laplace Beltrami Operator and cannot provide a stable basis that guarantee that the extracted signatures for the different patches are directly comparable. Interestingly, the accuracy improvement brought by GLFs is obtained also at a lower computational cost. Considering the extraction of patches as a common step between the three compared approaches, the curves-based framework requires a costly elastic deformation between corresponding curves (e.g. based on splines) and Shape-DNA requires computing an eigen-decomposition of every new patch to be analyzed. In contrast, GLFs only require the projection of the patch geometry into the Graph Laplacian eigenspace, which is common to all patches and can therefore be pre-computed off-line. We also show that 14 automatically detected landmarks are enough to achieve high FER and AU detection rates, only slightly below those obtained when using sets of manually annotated landmarks.