Facial Recognition with PCA and Machine Learning Methods

被引：0

作者：

Chen, Jiachen ^{[1
]}

Jenkins, W. Kenneth ^{[1
]}

机构：

[1] Penn State Univ, Sch Elect Engn & Comp Sci, University Pk, PA 16802 USA

来源：

2017 IEEE 60TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS) | 2017年

关键词：

Facial Recognition; Principle Component Analysis; Eigenface; Linear Discriminant Analysis; K Nearest Neighbor; Support Vector Machine; NEAREST NEIGHBOR CLASSIFICATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.

引用

页码：973 / 976

页数：4

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