Open AccessResearch on Face Recognition Algorithm Based on Multi-Class Support Vector Machine
Author(s) -
Liuxun Xue,
Hui Li,
Ping Wang,
Zhijie Lin,
Huanyu Li,
Xu Jie,
Chongyue Shi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012049
Subject(s) - artificial intelligence , normalization (sociology) , computer science , three dimensional face recognition , support vector machine , facial recognition system , pattern recognition (psychology) , biometrics , feature extraction , histogram , face detection , face (sociological concept) , computer vision , grayscale , histogram of oriented gradients , image processing , image (mathematics) , social science , sociology , anthropology
Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.