Machine Learning Approaches to Facial Recognition: A Survey

Due to the rapid development of technology, face recognition has received much attention recently. The face continues to be the most challenging study topic for experts in computer vision and image processing since it has unique characteristics that must be recognized as an entity. This survey explores the most challenging face aspects, including position change, age, lighting, and occlusion. They are considered essential elements in facial recognition systems when used with facial images. The current state of face detection methods and techniques is also examined in this paper. These methods and techniques include Eigenface, Artificial Neural Network (ANN), Support Vector Machine (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, 3D morphable models, and Hidden Markov Model (HMM). However, the goal of this study is to give a comprehensive assessment of the literature on face recognition and its applications. After a thorough discussion, the most important findings are presented.