Face recognition method and system of SVM (Support Vector Machine) based on PCA (Principal Component Analysis) and ReliefF, storage medium and equipment

Abstract

The invention discloses a face recognition method and system of an SVM based on PCA and ReliefF, a storage medium and equipment, applied to the field of image recognition. The method comprises the steps: converting a face image into a preset dimension vector, and storing the preset dimension vector into a face vector set; calculating a vector accumulation average value to obtain an average image; calculating a difference value between the face image and the average image, calculating a feature vector of a covariance matrix according to the difference value, and forming a feature face space; respectively calculating the feature weight of each feature face space sample according to a ReliefF algorithm, and constructing a feature weight vector; constructing and solving an optimal solution according to a preset penalty parameter and the feature weight vector, obtaining a decision function, and determining a support vector machine; and training and verifying based on a support vector machine to obtain a face recognition model, and performing face recognition by using the face recognition model. According to the technical scheme, the defect that a single ReliefF algorithm cannot remove redundant features is overcome, the data dimension is effectively reduced, and SVM training is accelerated.

Description

technical field [0001] The present invention relates to the technical field of image recognition, in particular to a face recognition method based on SVM of PCA and ReliefF, a face recognition system based on SVM of PCA and ReliefF, a computer-readable storage medium and an electronic device . Background technique [0002] The application of face recognition technology is becoming more and more widespread, covering all aspects of life, but there is still room for improvement in traditional face recognition algorithms, including feature extraction, image dimension control, recognition accuracy, and recognition efficiency. [0003] Traditional face recognition technology usually extracts eigenfaces by reducing the dimensionality of face images, and compares them with eigenface data. The existing classic algorithm Principal Component Analysis (PCA) is to obtain the principal components of the face by reducing the dimensionality of the image, and to obtain the eigenface by deco...

AI Technical Summary

Problems solved by technology

[0004] In view of the above problems, the present invention provides a face recognition method, system, storage medium and equipment based on PCA and ReliefF's SVM, by combining the feature selection method of PCA and ReliefF, using PCA to remove the correlation existing between features, Then use the ReliefF algorithm for feature selection, which solves the disadvantage that a single ReliefF algorithm cannot remove redundant features, so that it can effectively reduce the data dimension and accelerate SVM training while ensuring the SVM classification accuracy, with better performance.

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