Face Recognition Based on Principal Component Analysis and Support Vector Machine Algorithms

In order to improve the efficiency of face recognition, a face recognition method based on principal component analysis and support vector machine is proposed. Principal component analysis is used to transform the face image into a new feature space, which can reduce the dimension of feature space and eliminate the correlation and noise between image features. Then, a classification algorithm is obtained by using support vector machine algorithm. The test set is classified, and the probability that the classification probability is greater than the given threshold is added to the training set as the true value to improve the prior information of the target. Through the iterative use of support vector machine, a better recognition effect is obtained. In the open face database, the detection accuracy is improved by 5% compared with the classical algorithm.