Open AccessFace Acknowledgment using Principle Component Analysis (PCA) of Eigenfaces
Author(s) -
Muhammad Sajid Khane*,
Andrew Ware,
Abdullah Ayub Khan
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2861.059720
Subject(s) - eigenface , acknowledgement , biometrics , computer science , artificial intelligence , face (sociological concept) , computer vision , identity (music) , component (thermodynamics) , facial recognition system , histogram equalization , matching (statistics) , pattern recognition (psychology) , histogram , image (mathematics) , mathematics , computer security , statistics , social science , physics , sociology , acoustics , thermodynamics
Face acknowledgement is a biometric framework used to recognize or check an individual’s identity against a dataset of images. In recent times, there have been several different approaches utilized to try to achieve high accuracy rates. This paper presents a system that enables an individual’s identity to be determined based on a matching of their facial structure against a previously stored database. The matching compares the frontal view of the face with the two-dimensional images of the head already stored. In our system, the input image is sometimes enhanced using histogram equalization, before the matching takes place using the Euclidean distance between the face to be identified and those already stored. The developed acknowledgement system provides an accuracy of 97.5%.