The Research Of Face Recognition Algorithm Based On Geometric Features

The technology of human face recognition as a multi-disciplinary field and challenging psubject which contains digital image processing, pattern recognition, computer vision, neural network, psychology, physiology, mathematics and a good many subjects. In the meantime, it also has widely used. In the field of face recognition, the method of human face recognition based on geometric features has been paid great attention for its simple calculation and availability. At present, it has become one of the dominant methods as the feature extraction and recognition. This article locates human face organs, through apriori knowledge of human face topological structure geometrical relationship, making use of method based on construct to extract the features of human face organs, expressing human face through a set of geometric feature vectors. The recognition putting in summary is matched with feature vector.This paper includes the following parts:(1) Have a detailed introduction and analysis about the theory of greyscale integrated projection. This method is now the main method of locating human face. We put forward a new method called greyscale differential projection which is based on the previous method and locating the contour of human face vertical directly. Projection method is essentially based on statistics. It combines the apriori knowledge of human face feature distribution in the application. This method needn't to do any pretreatment to the image and any smoothing treatment to the integrated projection image. So this algorithm is simple; the accuracy is high; the speed is quick.(2) Give an introduction about the method of locating eyes precisely. This algorithm combines the character of the eye area greyscale totally distribution and greyscale transformation; combines the methods of traditional integrated projection and differential projection. The experiment led to the fact that this algorithm is not sensitive to the illumination transformation and has a high accuracy. Using greyscale integrated projection combines the apriori knowledge of human face character to locate nose. This location method also has high accuracy. The location of mouth is abtained through projection method.(3) The choice of characteristic points needs enough information and can't go so far as to increase calculation quantity. This article chooses seven characteristic points, namely, four canthus points, tip of nose and two corners of mouth points. Construct ten eigenvectors using them and carries on the normalization calculation to them. The last process of image recognition is classification. After adopting some standards to extract feature of human images, we construct category separability decision rule according to these characters and design classifier. This article takes use of minimum distance classification to classify. It is more suitable for human face recognition and calculation using weighing ratio to calculate similarity. How to choose a suitable recognition threshold is a difficult problem and need further research. This article ascertains it through a good many experiments.