Face Acknowledgment using Principle Component Analysis (PCA) of Eigenfaces
Muhammad Sajid Khan1, Andrew Ware2, Abdullah Khan3

1Muhammad Sajid Khane*, Army Public College of Management & Sciences, Rawalpindi, Pakistan.
2Andrew Ware, University of South Wales, United Kingdom.
3Abdullah Khan, Army Public College of Management & Sciences, Rawalpindi, Pakistan.
Manuscript received on April 20, 2020. | Revised Manuscript received on May 02, 2020. | Manuscript published on May 10, 2020. | PP: 1196-1200 | Volume-9 Issue-7, May 2020. | Retrieval Number: E2861039520/2020©BEIESP | DOI: 10.35940/ijitee.E2861.059720
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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%. 
Keywords: Identification, Acknowledgement, Recognition, Euclidean distance.
Scope of the Article: Pattern Recognition