Abstract
One of the challenges in pattern recognition technologies, especially face recognition, is the ability to handle scenarios where subjects are non-cooperative in terms of position (face pose) or deformation (face expression). In this paper, we propose an innovative approach for 3D face recognition that combines heterogeneous features by using evidence theory based on belief functions. The first feature is generated via wavelet network algorithm, which approximates every face, by an optimal linear combination. The second feature models each facial surface by a collection of facial curves based on geodesic distance. The fusion procedure adopt a refined model of belief function based on the Deampster-Shafer rule in the context of confusion matrix. Experimental evaluation performed on subset of the FRGC v2 database, shows that the recognition rate increases with fusion of redundant and/or independent data. Further, the technique demonstrates robustness under different facial expressions.
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Borgi, M.A., El’Arbi, M., Ben Amar, C. (2013). Wavelet Network and Geometric Features Fusion Using Belief Functions for 3D Face Recognition. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_38
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DOI: https://doi.org/10.1007/978-3-642-40246-3_38
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