"Distribution of PCA Face Coefficients in the Feature Space at an Increasing Number of Persons and Eigenfaces"
Distribution of PCA Face Coefficients in the Feature Space at an Increasing Number of Persons and Eigenfaces
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Granting access to security areas, authentication of a user to withdraw money from his/her bank account, or personalization of software products for a better user-machinge-interaction-these are just a few examples when people must be identified by a computer system. Nowadays, biometric systems, which measure and statistically analyze biological data, are more and more used for such a task. There are various different approaches to identify people. Fingerprints and iris recognition are very famous examples. Although these techniques are well suited for high security applications, they are intrusive both physically and socially. This means that users must position their bodies relative to any sensor and wait for a while in order to get identified. Therefore other approaches without such cahracteristic are required for user friendly interactions. At this point, face recognition comes into play. It is besides voice recognition one of the most famous approaches that fulfill this criterion.