Face Recognition on Bionic Pattern

Abstract

A "matter cognition" based face recognition model has been proposed. By taking continuity rule of samples of a same class as the starting point, face pattern recognition is considered as face pattern cognition instead of its classification. Compared with traditional best classification goaled statistic pattern recognition, it's more similar to the character of human cognition. A person's face distribution in low dimension space has a certain kind of cohesion, while face coverage of different people overlap. By the increase of space dimension, the cohesion of samples of a same class decrease, while the repel of samples of different classes increases. But as the increase of space dimension continue, both the cohesion of samples of a same class and the repel of samples of different classes decreases. Coverage of candidate faces recognition is processed in a certain space. If it belongs to several candidate face coverage, Fisher method can be applied to get the final result. Experiment based on ORL proves that, random object without training can be perfectly recognized. The recognition rate can be as high as 97.5%