The Impact of the Number of Eigen-Faces on the Face Recognition Accuracy Using Different Distance Measures
Source of Publication
2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)
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 eigenfaces highly depends on the selected distance measure.
Institute of Electrical and Electronics Engineers (IEEE)
Shatnawi, Yousef; Alsmirat, Mohammad; Al-Ayyoub, Mahmoud; and Aldwairi, Monther, "The Impact of the Number of Eigen-Faces on the Face Recognition Accuracy Using Different Distance Measures" (2018). All Works. 3487.
Indexed in Scopus