Naive Algorithm for efficient identification of human face using Machine Learning

Abstract

A consolidated Linear Discriminant Analysis (LDA)/Principal Component Analysis (PCA) methodology for face recognition is put forward in this research to immediately maximise the LDA criterion without having to look for a further PCA step. In certain circumstances, the aforementioned approach is identical to the eigenface (PCA) technique and eliminates the possibility of information loss associated with a second PCA phase. The outcomes of the experiment show that the suggested algorithm is workable. The broader context underscores the necessity of computer vision, especially in the area of recognition of faces, which has uses in anything from tracking attendance to surveillance.

The paper investigates several machine learning methods, such as Eigenface, Fisherface, Convolutional Neural Network, Support Vector Machine, and Principal Component Analysis, in the context of utilising Facial Recognition. With the application of Convolutional Neural Network, the efficiency of existing algorithms can be improved.