Performance Analysis of Face Recognition using Eigenface Approach

Eigenface is an algorithm in the principal component analysis (PCA) that is used to recognize faces. Eigenface used to reduce dimensionality and find the best vector for distributing the facial image in the facial space. This method has been widely used and implemented in various previous researches to recognize human face images. Not only to detect human faces under normal conditions, but PCA has also been proven to be able to properly recognize images in various expressions. It can even recognize facial images with various challenges such as detecting faces after plastic surgery and combining them with facial image reconstruction techniques. This research aims to examine the performance of the PCA-Eigenface method to recognize human face images from several databases that have their own challenges, such as the lack of illumination of facial images, significant variations in expression and the use of accessories such as glasses. The recognizable accuracy is quite varied, from 100% to 67% in each database with and with an average recognition of more than 85%.