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Difference-Based Local Gradient Patterns for Image Representation

  • Shimaa Saad
  • Alaa SagheerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

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.

Keywords

Local gradient patterns Face identification Face non-face classification 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science, College of Computer Science and Information TechnologyKing Faisal UniversityHofufKingdome of Saudi Arabia
  2. 2.Center for Artificial Intelligence and Robotics, Faculty of ScienceAswan UniversityAswanEgypt

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