Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Biometric authentication utilizing Face Recognition (FR) is emerging as a significant research field. In this scenario, Linear Collaborative Discriminant Regression Classification (LCDRC) scheme is undertaken for experimental examination. Whereas, LCDRC could not able to categorize the samples that scattered around the intersections and also it gives a poor outcome in severe lighting variations. In order to overcome this difficulties, an effective weight function along with Deep Learning (DL) is included in LCDRC. Respective weight function is selected based on Modified-Particle Swarm Optimization (MPSO) algorithm. This proposed methodology significantly maximize the Reconstruction Error (RE) between the classes and also it minimize the RE within the class. Though, the proposed methodology not only out-performs LCDRC, also it provides superior outcome in terms of accuracy.
How to cite this article:
K. Shailaja and B. Anuradha. Improved Face Recognition using a Modified PSO Based Self-Weighted
Linear Collaborative Discriminant Regression Classification.
DOI: https://doi.org/10.36478/jeasci.2017.7234.7241
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2017.7234.7241