Analytical Study of Face Recognition Methods Using Machine Learning

Abstract

Many facial recognition algorithms, as well as their adaptations, have previously been devised during the last decades. A number is indicated for standard algorithms. They are divided into two categories: appearance-based and model-based. Three distinct linear subspace analysis strategies for appearance-based processes are presented, along with a brief examination of numerous non-linear manifold analysis approaches for face recognition. Elastic Bunch Graph matching, Active Appearance Model, and 3D Model processes are discussed. A vast variety of publicly available face datasets and performance evaluation ratings are examined. Based on previous recognition outcomes, more study areas are recommended. Face recognition has piqued the interest of academics in biometrics, pattern recognition, and computer vision. Previous research in this field had a basic fault in that it was designed to recognize just single faces, resulting in a long detection time and additional time for annotation. The goal of this study is to create a face recognition system that makes use of a machine learning algorithm and principal component analysis (PCA). The study makes use of linear discriminant analysis, multilayer perceptrons, Naive Bayes, and support vector machines. Furthermore, it obtained 97% and 100% identification accuracy, respectively, using PCA and linear discriminant analysis. The extensive comprehension CNNs are the most frequent form of machine learning algorithm used for facial recognition. CNNs are a sort of artificial neural network that performs exceptionally well at image categorization. Face recognition is a popular topic in artificial intelligence. This program was widely used in our daily life. Several face recognition devices were installed on phones to protect private information and utilized on Facebook to quickly recognize when Facebook users appeared in photographs. Several face recognition algorithms have previously been developed; yet, it remains incredibly difficult in real-world situations. A basic technique for differentiating people is influenced by factors such as partial facial occlusion, illumination, and posture variance.