An Evaluation of Performance of k-NN and Mahalanobis Distance on Local Binary Pattern Feature Representation for Access Control in Face Recognition System

Abstract

A vast majority of the existing face recognition techniques encounter misclassifications when there is a large variation of the subject in terms of pose, illumination and expression as well as artefact such as occlusion. With the usage of Local Binary Pattern (LBP), local feature representation which preserves local primitives and texture information, robust against illumination, expression and occlusion was realized in the feature extraction process. Mahalanobis distance and k-NN classifier were employed for recognition as basis for comparison. An assessment of both algorithms was considered. It was observed that Mahalanobis distance measure outperformed the k-NN classifier for access control and recognition system due to its high recognition rate (98.4%), genuine acceptance rate (98.37) and its stern adherence to both FAR and FRR (0.0144, 1.568) than kNN with 94.9%

and (0.392 and 2.895) respectively. This result demonstrates the robustness of Mahalanobis distance classifier for

use access control system.

Keywords - Information and Communications Technology, devices, infusion modeling, Nigeria, private transport companies.