Discriminat Components embedded in subspace for face recognition

被引：0

作者：

Guan, Y. D. ^{[1
]}

Zhu, R. F. ^{[1
]}

Ma, G. K. ^{[1
]}

Wang, Q. W. ^{[1
]}

Wu, M. D. ^{[1
]}

机构：

[1] Harbin Inst Technol, 92 West Da Zhi St, Harbin, Peoples R China

来源：

2015 FIFTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC) | 2015年

关键词：

Discriminat Components; Linear Discriminant Analysis; feature extraction; generative probability model; face recognition;

D O I：

10.1109/IMCCC.2015.341

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Face recognition is a promising biometrics resource. In this paper, for robust and discriminative to change in face recognition, we introduce an algorithm that Linear Discriminant Analysis is applied to the discriminative components, which feature extraction based on the generative probability model and use the distance-based similarity measures for face recognition. XM2VTS dataset is used to validate that the proposed method is superior to the classic algorithms, such as probabilistic Linear Discriminant Analysis, Bayes algorithm and many state-of-the-art linear subspace learning (LSL) algorithms. In particular, our method achieves 98% face recognition rate.

引用

页码：1606 / 1611

页数：6

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