A Bespoke Approach For Face-Recognition Using PCA

In this paper we have developed a bespoke approach to face recognition with Eigenfaces using principal component analysis. We have focused on the effects of taking the number of significant eigenfaces. Eigenfaces approach is a principal component analysis method, in which a small set of characteristic pictures are used to describe the variation between face images. Experimental results using MATLAB are demonstrated in this paper to verify the viability of the proposed face recognition method. It shows that only 15% of Eigenfaces with the largest eigen values are sufficient for the recognition of a person. It also shows that if the minimum Euclidian distance of the test image from other images is zero, then the test image completely matches the existing image in the database. If minimum Euclidian distance is non-zero but less than threshold value, then it is a known face but having different face expression else it is an unknown face.