一种融合Gabor+SIFT特征的人脸识别算法

doi:10.16180/j.cnki.issn1007-7820.2019.04.001

Abstract

Abstract:

Position variation and illumination interference affect the accuracy and efficiency of face recognition. To solve the problem, a new method of feature extraction combining the Gabor feature and SIFT feature was proposed in this paper. Gabor feature of face images from multiple scales and directions were extracted and divided into sub-images of the same size. The SIFT features were extracted from the partitioned sub-images, and all SIFT vectors of the obtained Gabor features were cascaded as the final feature vectors. Principal component analysis method was used to reduce the dimension of the final eigenvectors, and then the least squares support vector machine was used for training and recognition of images. Experimental results of tests in the FERET face database showed that compared with the traditional single face recognition method, the accuracy of face recognition was 98.1% under the change of position and illumination interference, which proved the effectiveness of the new algorithm.

Key words: face recognition, Gabor feature, SIFT feature, feature point matching, PCA, SVM

CLC Number:

TN957.52

ZHOU Zhu,GAN Yi,SUN Fujia. Research of Face Recognition Method Based on Gabor and SIFT Features[J].Electronic Science and Technology, 2019, 32(4): 1-5.

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References 17

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样本数量	平均识别率
样本数量	文献[15]	文献[16]	文献[5]	文献[17]	本文算法
2	92.9%	89.9%	90.2%	90.8%	92.5%
4	95.7%	90.7%	91.9%	91.3%	93.9%
6	96.8%	91.2%	93.7%	92.2%	96.3%
8	98.5%	94.4%	94.9%	93.6%	97.8%

样本数量	平均识别率
样本数量	文献[15]	文献[16]	文献[5]	文献[17]	本文算法
2	85.2%	90.2%	81.1%	82.7%	92.5%
4	86.4%	93.8%	83.5%	83.6%	94.9%
6	87.8%	94.3%	86.2%	85.3%	97.3%
8	89.4%	96.6%	88.5%	87.9%	98.1%

Research of Face Recognition Method Based on Gabor and SIFT Features

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