KPCA and Eigen Face Based Dimension Reduction Face Recognition Method

The practice of identifying people depending on their facial traits is known as face recognition. With rising security demands and technological advancements, obtaining information has gotten much easier. This work creates a face identification application that compares the results of several methods. The main goal is to recognize the face and retrieve information from a database. There are two main steps to it. The first stage is to discover the distinguishing elements in an image and save them. The second step is to compare it to existing photographs and provide the data associated with that image. Face detection algorithms include the Principal Component Analysis PCA mechanism and Eigen Face. To lower the complexity of image identification, we employed Kernel PCA to identify the number of components in an image and further lowered the dimension data set. Experimental results prove that if we reduced the number of dimensions of an image, then a smaller number of Eigen faces will be produced and it will take less time to identify an image. Jasdeep Singh | Kirti Bhatia | Shalini Bhadola "KPCA and Eigen-Face-Based Dimension Reduction Face Recognition Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50507.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/50507/kpca-and-eigenfacebased-dimension-reduction-face-recognition-method/jasdeep-singh