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31 May 2013 Local gradient Gabor pattern (LGGP) with applications in face recognition, cross-spectral matching, and soft biometrics
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Abstract
Researchers in face recognition have been using Gabor filters for image representation due to their robustness to complex variations in expression and illumination. Numerous methods have been proposed to model the output of filter responses by employing either local or global descriptors. In this work, we propose a novel but simple approach for encoding Gradient information on Gabor-transformed images to represent the face, which can be used for identity, gender and ethnicity assessment. Extensive experiments on the standard face benchmark FERET (Visible versus Visible), as well as the heterogeneous face dataset HFB (Near-infrared versus Visible), suggest that the matching performance due to the proposed descriptor is comparable against state-of-the-art descriptor-based approaches in face recognition applications. Furthermore, the same feature set is used in the framework of a Collaborative Representation Classification (CRC) scheme for deducing soft biometric traits such as gender and ethnicity from face images in the AR, Morph and CAS-PEAL databases.
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Cunjian Chen and Arun Ross "Local gradient Gabor pattern (LGGP) with applications in face recognition, cross-spectral matching, and soft biometrics", Proc. SPIE 8712, Biometric and Surveillance Technology for Human and Activity Identification X, 87120R (31 May 2013); https://doi.org/10.1117/12.2018230
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