Naemeh Mosavi - Graduate Student Artificial intelligence, Islamic Azad University, Kerman, Iran
Alireza Zerack - Head of Department Iran's Atomic Energy Research Institute

Tface plays a major role in our social intercourse in conveying identity and emotions. The human ability to recognize faces is remarkable. We can recognize thousands of faces learned throughout our lifetime and identify familiar faces at a glance even after years of separation. The skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expressions, aging, and distractions such as glasses or changes in hairstyle. But developing a computational model of face recognition is quite difficult, because faces are complex, multidimensional, and subject to change over time. Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic (as opposed to a parts-based or feature-based) manner. Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with N pixels is considered a point (or vector) in N-dimensional space. The Eigenface approach is considered by many to be the first working facial recognition technology, and it served as the basis for one of the top commercial face recognition technology products. Since its initial development and publication, there have been many extensions to the original method and many new developments in automatic face recognition systems. Eigenfaces is still often considered as a baseline comparison method to demonstrate the minimum expected performance of such a system

Eigenface, Face Image, Rebuilding, recognition