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Robust Face Recognition with Structural Binary Gradient Patterns

This paper presents a computationally efficient yet powerful binary framework for robust facial representation based on image gradients. It is termed as structural binary gradient patterns (SBGP). To discover underlying local structures in the gradient domain, we compute image gradients from multiple directions and simplify them into a set of binary strings. The SBGP is derived from certain types of these binary strings that have meaningful local structures and are capable of resembling fundamental textural information. They detect micro orientational edges and possess strong orientation and locality capabilities, thus enabling great discrimination. The SBGP also benefits from the advantages of the gradient domain and exhibits profound robustness against illumination variations. The binary strategy realized by pixel correlations in a small neighborhood substantially simplifies the computational complexity and achieves extremely efficient processing with only 0.0032s in Matlab for a typical face image. Furthermore, the discrimination power of the SBGP can be enhanced on a set of defined orientational image gradient magnitudes, further enforcing locality and orientation. Results of extensive experiments on various benchmark databases illustrate significant improvements of the SBGP based representations over the existing state-of-the-art local descriptors in the terms of discrimination, robustness and complexity. Codes for the SBGP methods will be available at http://www.eee.manchester.ac.uk/research/groups/sisp/software/.

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