Analysis of Face Feature Recognition using MATLAB

One of the fastest and simplest ways to distinguish between two persons who seem identical is to look at their face. Face recognition is a kind of personal identification system that uses an individual's physical traits to determine their identity. Face detection, which occurs quickly in people (unless in situations when the item is nearby), and face recognition are the main steps in the process of identifying a face. The second phase, dubbed "introduction," is proving that a certain person is connected to a specific face. Then, using a duplicate of the scene, a model for facial image recognition—also known as face recognition—is created. One of the biometrics technologies that has attracted the most research focus is facial recognition. The Fisherface technique and the Eigenface method are two examples of the several techniques utilized in the established facial recognition patterns of today. The conceptual basis of the technique is the Principal Component Analysis (PCA), which is used to minimize the face-dimensional space occupied by facial features. Finding the eigenvector that corresponds to the face image's biggest eigenvalue was the main goal of principal component analysis (PCA) when it came to face recognition using Eigen faces (facial space). Image processing will be a major component of the face detection and identification system that is being developed for this project. Software called Matlab is necessary for this task.