Facial Recognition System
A facial recognition system is a computer driven application for automatically identifying a person from a digital image. It does that by comparing selected facial features in the live image and a facial database.
It is typically used for security systems and can be compared to other biometrics such as fingerprint or eye-iris recognition systems. The London Borough of Newham, in the UK, has a facial recognition system built into their borough-wide CCTV system: closed-circuit television system.
Popular recognition algorithms include "Eigenface," "Fisherface" and the "Hidden Markov model." Critics of the technology complain that the London Borough of Newham scheme has, as of 2004, never recognised a single criminal, despite several criminals in the system's database living in the Borough, and the system having been running for several years. An experiment by the local police department in Tampa, Florida, had similarly disappointing results.
Eigenface Eigenfaces are a set of eigenvectors derived from the covariance matrix of the probability distribution of the high-dimensional vector space of possible faces of human beings. These eigen vectors are used in the computer vision problem of human face recognition.
In layperson's terms, eigenfaces are a set of "standardized face ingredients," derived from statistical analysis of many pictures of faces. Any human face can be considered to be a combination of these standard faces. One person's face might be made up of 10% from face 1, 24% from face 2 and so on. This means that if you want to record someone's face for use by face recognition software, you can use far less space than would be taken up by a digitized photograph. To generate a set of eigenfaces, a large set of digitized images of human faces, taken under the same lighting conditions, are normalized to line up the eyes and mouths. They are then all resampled at the same pixel resolution (say m×n), and then treated as mn-dimensional vectors whose components are the values of their pixels. The eigenvectors of the covariance matrix of the statistical distribution of face image vectors are then extracted.
Since the eigen vectors belong to the same vector space as face images, they can be viewed as if they were m×n pixel face images: hence the name eigenfaces.
Viewed in this way, the principal eigenface looks like a bland androgynous average human face. Some subsequent eigenfaces can be seen to correspond to generalized features such as left-right and top-bottom, or the presence or lack of a beard.
When properly weighted, eigenfaces can be summed together to create an approximate gray-scale rendering of a human face. Remarkably few eigenvector terms are needed to give a fair likeness of most people's faces, so eigenfaces provide a means of applying data compression to faces for identification purposes.
