The global features of face image have been extensively used for face recognition however they are sensitive to variations caused by expressions, illumination, pose, occlusions and makeup.
The paper describes the enhancement in the behavior of the 2D PCA (Principles Component Analysis) based recognition algorithm that recognize face images by adding noise removal filter before and after the recognition stage, PCA algorithm based on information theory concept, seeks a computational model that best describes a face by extracting the most relevant information contained, and compare the eigenface with the eigenfaces in the gallery database, the euclidean distance check the face image acceptance with noise removal filter added as an additional step to modify the performance of classic PCA algorithm to get better recognition.