GA-based optimal selection of PZMI features for face recognition

被引：30

作者：

Kanan, Hamidreza Rashidy ^{[1
]}

Faez, Karim ^{[1
]}

机构：

[1] Amirkabir Univ Technol, Dept Elect Engn, Machine Vis Lab, Tehran 15914, Iran

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2008年 / 205卷 / 02期

关键词：

Face recognition; Feature selection; Pseudo Zernike moment invariant (PZMI); Genetic algorithm (GA);

D O I：

10.1016/j.amc.2008.05.114

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

One of the key problems in automated face recognition system is that of handling the face image variation in terms of scale, rotation (in plane) and translation. One approach is. xing mentioned problems in recognition processes by extracting one linear transformation invariant feature. This paper presents a novel method for face recognition. Pseudo Zernike moment invariant (PZMI) which has linear transformation invariance properties and is robust in the presence of noise utilized to produce feature vectors. For decreasing computational complexity of feature extraction step, we use genetic algorithm (GA) to select the optimal feature set which contains optimal PZMI orders and corresponding repetitions. In addition, we have investigated the effect of PZMI orders on recognition rate in noisy images. Proposed scheme has been tested on the FERET database. Experimental results prove the advantages of the proposed method when compared with other PZMI-based face recognition systems. (C) 2008 Elsevier Inc. All rights reserved.

引用

页码：706 / 715

页数：10

共 50 条

[1] A GA-based optimal gene subset selection method
Ding, SC
Liu, J
Yang, Q
[J]. PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 2784 - 2789
[2] Optimizing Selection of PZMI Features Based on MMAS Algorithm for Face Recognition of the Online Video Contextual Advertisement User-Oriented System
Bao Nguyen Le
Dac-Nhuong Le
Gia Nhu Nguyen
Do Nang Toan
[J]. INTEGRATED UNCERTAINTY IN KNOWLEDGE MODELLING AND DECISION MAKING, IUKM 2016, 2016, 9978 : 317 - 330
[3] GA-based Parameterization and Feature Selection for Automatic Music Genre Recognition
Serwach, Marcin
Stasiak, Bartlomiej
[J]. PROCEEDINGS OF 2016 17TH INTERNATIONAL CONFERENCE COMPUTATIONAL PROBLEMS OF ELECTRICAL ENGINEERING (CPEE), 2016,
[4] A GA-based feature selection approach with an application to handwritten character recognition
De Stefano, C.
Fontanella, F.
Marrocco, C.
di Freca, A. Scotto
[J]. PATTERN RECOGNITION LETTERS, 2014, 35 : 130 - 141
[5] GA-Based Selection of Vaginal Microbiome Features Associated with Bacterial Vaginosis
Carter, Joi
Beck, Daniel
Williams, Henry
Dozier, Gerry
Foster, James A.
[J]. GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2014, : 265 - 268
[6] A Ga-based negative selection algorithm
Gao, Xiao-Zhi
Ovaska, Seppo Jari
Wang, Xiaolei
[J]. INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2008, 4 (04): : 971 - 979
[7] PZMI and wavelet transform features in face recognition system using a new localization method
Kanan, HR
Faez, K
[J]. IECON 2005: THIRTY-FIRST ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-3, 2005, : 2690 - 2694
[8] Using GA-based Feature Selection for Emotion Recognition from Physiological Signals
Gu, Y.
Tan, S. L.
Wong, K. J.
Ho, M. H. R.
Qu, L.
[J]. 2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATIONS SYSTEMS (ISPACS 2008), 2008, : 70 - +
[9] A face recognition algorithm based on optimal feature selection
Zhao, Kai
Wang, Dan
Wang, Yi
[J]. Revue d'Intelligence Artificielle, 2019, 33 (02) : 105 - 109
[10] Pixel Selection in a Face Image based on Discriminant Features for Face Recognition
Choi, Sang-Il
Choi, Chong-Ho
Jeong, Gu-Min
[J]. 2008 8TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2008), VOLS 1 AND 2, 2008, : 84 - +

← 1 2 3 4 5 →