@inproceedings{08395f03202b466c8f9d1044b0c1e4d3,
title = "The Impact of the Number of Eigen-Faces on the Face Recognition Accuracy using Different Distance Measures",
abstract = "The embedded and real-time systems are the main motivation for this research where the computations are critical to be reduced as much as possible. Face recognition method using eigen-faces yields good accuracy if enough eigen-faces are considered in the classification process. The more eigen-faces used, the more computation power is needed. In this paper, the main goal is to investigate the trade-off between the used number of eigen-faces and the accuracy and the needed computation power of face recognition. Three different distance measures are studied. Namely: Euclidean, block-city, and chess board distances are used. It is concluded that there is some optimum number of eigen-faces that provides the highest recognition rate and acceptable execution time. Moreover, the best number of eigen-faces highly depends on the selected distance measure.",
keywords = "Distance Measures, Eigen-Faces, Face Recognition, PCA",
author = "Yousef Shatnawi and Mohammad Alsmirat and Mahmoud Al-Ayyoub and Monther Aldwairi",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018 ; Conference date: 28-10-2018 Through 01-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/AICCSA.2018.8612837",
language = "English",
series = "Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA",
publisher = "IEEE Computer Society",
booktitle = "2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018",
address = "United States",
}