A COMPARATIVE STUDY OF FACE RECOGNITION  BASED ON SELECTED REGIONS WITH PRINCIPAL COMPONENT ANALYSIS PCA AND KERNEL PRINCIPAL COMPONENT ANALYSIS KPCA AND GABOR FILTERS Cover Image

СРАВНИТЕЛНО РАЗПОЗАВАНЕ НА ЛИЦА СЪС СЕЛЕКТИРАНИ РЕГИОНИ С PRINCIPAL COMPONENT ANALYSIS PCA И KERNEL PRINCIPAL COMPONENT ANALYSIS KPCA И ФИЛТРИ НА ГАБОР
A COMPARATIVE STUDY OF FACE RECOGNITION BASED ON SELECTED REGIONS WITH PRINCIPAL COMPONENT ANALYSIS PCA AND KERNEL PRINCIPAL COMPONENT ANALYSIS KPCA AND GABOR FILTERS

Author(s): Petya Petrova
Subject(s): Social Sciences, Education, Communication studies, Theory of Communication, Higher Education
Published by: Бургаски свободен университет
Keywords: Comparative Face Recognition; Region of Interest; Recognition Rate

Summary/Abstract: The paper presents a comparative study of performance for face recognition algorithms using Principal Component Analysis PCA and Kernel Principal Component analysis KPCA. Images with various Regions of Interest ROIs are chosen from the databases to recognise faces. The results of parallel recognition are compared with results of ideal conditions. It has been established that the size of the ROIs affects the rate of recognition.

  • Issue Year: 6/2017
  • Issue No: 1
  • Page Range: 3-7
  • Page Count: 5
  • Language: Bulgarian