.png){kind=icon}
返回
Face recognition based on statistical features and SVM classifier
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-021-11816-w.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a face recognition method based on statistical features and Support Vector Machine (SVM) algorithm is proposed. The statistical analysis is used to extract and select the statistical features, whereas, the SVM algorithm is employed to merge and classify the different features in order to increase the quality of the information and to obtain an optimal Human face recognition. Human face recognition results from the proposed method are validated and the True Success Rate (TSR) for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results with 400 test images of 40 persons demonstrate the superiority of introducing the statistical features in SVM algorithm for human face recognition. In addition, the recognition speed of our method is faster than the classical SVM algorithm and other existing methods. Experimental results show that the algorithm identifies the face images with accuracy of 99.37%.
Keyword:
Face recognition
Feature extraction
Statistical features
Principal component analysis
True success rate
Support vector machine
Classification
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
.png){kind=icon}
Particle swarm optimization based block feature selection in face recognition system基于粒子群优化的分块特征选择在人脸识别系统中的应用
PREAI
分享收藏
查看更多内容