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Currently, the utilisation of biometric traits for the authentication of individuals has become widespread. Various biometric features such as fingerprints, iris patterns, and facial characteristics are employed for the purpose of person authentication. Facial recognition technology is widely recognised as a popular method for person authentication. There exists a variety of algorithms, each with its own set of advantages and disadvantages. Dimensionality reduction is a crucial step in facial recognition algorithms due to the presence of multiple facial features within a facial image. The primary objective of this paper is to employ principle component analysis techniques in the face recognition process. Principal Component Analysis (PCA) has been found to yield highly favourable outcomes in the context of dimensionality reduction. Principal Component Analysis (PCA) is a statistical technique that generates eigenvectors. The eigenvectors are combined to form images, which are subsequently used to visualise the eigenfaces.
Ambikapathy et al. (Fri,) studied this question.