Abstract
This paper aims to examine the impact of pixel differences on local gradient patterns (LGP) for representing facial images. Two difference-based descriptors are proposed, namely, the angular difference LGP (AD-LGP) and the radial difference LGP (RD-LGP) descriptors. For evaluation purpose, two experiments are conducted. The first is face/non face classification using samples from CMU-PIE and CBCL databases. The second is face identification under illumination variations using the extended Yale face database B and the CMU-PIE face database. The experimental results show that both descriptors demonstrate, generally, a higher capability in discriminating face patterns from non-face patterns than the standard LGP. However, in face identification, the AD-LGP descriptor shows robustness against illumination variations, while the performance of the RD-LGP descriptor degrades with hard illuminations. Furthermore, we enhance the RD-LGP descriptor using the Average-Before-Quantization (ABQ) approach in order to increase its robustness toward illumination changes.
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Saad, S., Sagheer, A. (2015). Difference-Based Local Gradient Patterns for Image Representation. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9280. Springer, Cham. https://doi.org/10.1007/978-3-319-23234-8_44
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DOI: https://doi.org/10.1007/978-3-319-23234-8_44
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