Face Recognition System with Automatic Training Samples Selection Using Self-organizing Map

被引：0

作者：

Jirka, Vojtech ^{[1
]}

Feder, Matej ^{[1
]}

Pavlovicova, Jarmila ^{[1
]}

Oravec, Miloc ^{[1
]}

机构：

[1] Slovak Univ Technol Bratislava, Fac Elect Engn & Informat Technol, Bratislava 81219, Slovakia

来源：

2014 56TH INTERNATIONAL SYMPOSIUM ELMAR (ELMAR) | 2014年

关键词：

biometry; biometric recognition; face recognition; training process; self-organizing map; clustering algorithm; PCA; SVM;

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：

The paper deals with evaluation of automatic training samples selection method based on self-organizing map (SOM) in face recognition systems. In earlier paper [1] we presented an approach for automatic training samples selection using various clustering algorithms with good results on the CMU PIE face database. We showed that with the use of SOM we can achieve a good training samples selection. In this paper we further evaluate this approach with the use of face recognition systems based on principal component analysis (PCA) and support vector machines (SVM). We compare the results with random (uncontrolled and controlled) training samples selection and we evaluate the recognition accuracy of each method.

引用

页码：23 / 26

页数：4

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