Face recognition optimization method based on samme.rcw algorithm

Abstract

The face recognition optimization method based on the SAMME.RCW algorithm first extracts the features of the face image, uses the image feature vector, and uses the SAMME.RCW algorithm for recognition and classification. The weight adjustment process of the SAMME.R algorithm is modified to ensure that the weight of each type of sample cannot be too small when resampling occurs, which also makes the weight adjustment after resampling more biased towards minority samples, ensuring Classification performance of these samples. The SAMME.R algorithm requires the performance of weak classifiers. The weight of the correctly classified samples in each category is greater than the weight of any other category of samples. It requires the correct rate for each category separately. By modifying the distribution of weights during resampling, it is ensured that the probability of each type of sample being selected is basically the same, and at the same time, the classification effect of the minority class and majority class samples in the weak classifier is guaranteed. The final strong classifier can effectively improve the accuracy of face recognition.

Description

technical field [0001] The invention belongs to the technical field of machine learning and pattern recognition, and integrates training data to construct a prediction method with strong generalization ability, so as to give accurate estimates to new unknown objects. Background technique [0002] Face recognition technology is an important technology in image processing, and it is an active research field in biometric recognition. Using computer vision and image processing technology, using the contour features and local detail features of the face to perform face recognition. At present, it has been applied in identity authentication and authority control. However, the low recognition rate is an important reason that has hindered the widespread application of face recognition technology. The research found that the accuracy rate can be improved through the method of integrated learning. Ensemble learning is a new machine learning paradigm that uses multiple base classifi...

AI Technical Summary

Problems solved by technology

[0004] The object of the present invention is to propose a kind of improved SAMME.R algorithm SAMME.RCW to be applied in the face recognition to the low recognition rate problem that traditional face recognition technology (KNN algorithm) exists

Images