Expediting Automated Face Recognition Using the Novel ORB2-IPR Framework

  • A. Vinay
  • Vinay S. Shekhar
  • N. Manjunath
  • K. N. Balasubramanya Murthy
  • S. Natarajan
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)

Abstract

Face Recognition (FR) is at the forefront of distinctly unresolved challenges in the domain of Computer Vision, due to the sharp accuracy and performance drops it undergoes, when there are pronounced variations in parameters such as illumination, pose, background clutter and so on between the input and database faces. In this paper, we attempt to expedite the performance of automated FR with real-time images, using a novel framework called ORB2-IPR (ORB based Bag of Interest Points using RANSAC), which exhaustively learns a vocabulary of highly discriminative facial interest points from the facial database images (which can be referred to, and compared directly, instead of following the conventional time-intensive approach of comparing a given input face with each database face separately) by employing the cost-effective ORB (Oriented Fast Rotated Brief) descriptor (instead of the commonly employed SIFT and SURF descriptors), followed by the application of RANSAC (Random Sample Consensus) as a post-processing step to remove noise in the form of outliers, in order to improve the accuracy of the system. We will conclusively demonstrate that our technique is capable of rendering superior performance than the state-of-the-art methodologies using extensive mathematical arguments and by carrying out ample experimentations on the benchmark ORL, Face 95 and LFW databases.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • A. Vinay
    • 1
  • Vinay S. Shekhar
    • 1
  • N. Manjunath
    • 1
  • K. N. Balasubramanya Murthy
    • 1
  • S. Natarajan
    • 1
  1. 1.PES UniversityBengaluruIndia

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