Face recognition using holistics features of low frequency infomation

Abstract

Face recognition has been getting the most attention from researchers in biometrics. Since the introduction of biometrics, it tried to find a process identified by comparing the current biometric pattern to the database. Here is a function of behavior in addition to the physiological much of which work in the program as an example of biometric fingerprint, iris scanning, signature, hand geometry and voice/speech. Man or woman can be separated as proof of identification in addition to the recognition will depend on your circumstances of the request. Some work on facial recognition has been successful method to identify facial features or remotely using a template that includes some of the area. A holistic technique used to identify characteristics or face geometry was introduced. Characterization advance achieved by random sampling of selected properties of the image pixels. From this information, we developed a data set corresponding to the low frequency values. The facial recognition systems for personal identification and validation using Principle Component Analysis (PCA) are among the most common technique for feature extraction technique used in face recognition. The test results in a database interface Olivetti Research Laboratory (ORL) to produce interesting results from the point of view of the recognition success, rate, and the robustness of face recognition algorithms.