Abstract
The extraction of invariant features is the core of face recognition systems (FR). In this chapter, we propose a new and efficient facial image representation based on Gabor energy filters (GFs) and Complex Zernike moments (ZMs) in which GFs are used to extract texture features while ZMs are employed to extract the shape features. Most existing methods only use magnitude component of the ZMs (respectively GFs) as features in recognition task. Recently, it has become well-known that the phase component of moments (respectively Gabor Filters) also captures useful information for image representation. Next, the extracted features vectors are projected onto a low-dimensional subspace using Kernel Fisher Analysis (KFA) technique. Then, a comprehensive performance evaluation of these approach is achieved on the most popular benchmark FERET Database for face identification scenarios.
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Ouanan, H., Diouri, O., Gaga, A., Ouanan, M., Aksasse, B. (2020). A Novel Face Recognition System Based on Gabor and Zernike Features. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1106. Springer, Cham. https://doi.org/10.1007/978-3-030-36677-3_2
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