Comparative Analysis of PCA-Based and Neural Network Based Face Recognition Systems

被引：0

作者：

Adebayo, Kolawole John ^{[1
]}

Onifade, Olufade Williams ^{[2
]}

Yisa, Fatai Idowu ^{[1
]}

机构：

[1] Oduduwa Univ, Dept Comp Sci, Ife, Nigeria

[2] Univ Ibadan, Dept Comp Sci, Ibadan, Nigeria

来源：

2012 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA) | 2012年

关键词：

Principal Component Analysis; 2 dimensional PCA; Biometric; Artificial Neural Network; False Acceptance Rate; False Rejection Rate; Face recognition; FEATURE-EXTRACTION; 2-DIMENSIONAL PCA; IMAGE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

the continuous growth of insecurity issues around the world has further increased public interest in biometric surveillance systems. Face recognition has proven to be definitive in this area due to its low intrusiveness, accuracy and finesse unlike other biometric systems. This paper presents a comparative analysis of the performance of some selected face recognition systems, namely the PCA, 2DPCA and Artificial Neural Network. The algorithms were implemented and tested exhaustively to evaluate the performance of these algorithms under different face databases in respect to false acceptance rate and false rejection rate.

引用

页码：28 / 33

页数：6

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