Face Recognition Method by Using Large and Representative Datasets

被引：1

作者：

Zhao Tongzhou ^{[1
]}

Wang Yanli ^{[1
]}

Wang Haihui ^{[1
]}

Gao Sheng ^{[1
]}

Song Hongxian ^{[1
]}

机构：

[1] Wuhan Inst Technol, Sch Comp Sci & Engn, Wuhan 430073, Peoples R China

来源：

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年

关键词：

Face Recognition; Representative Datasets; Principal Component Analysis; Pattern Recognition; DISCRIMINANT-ANALYSIS; COMPONENT ANALYSIS; PCA;

D O I：

10.1109/CCDC.2009.5194964

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A face recognition method by using large and representative datasets is presented in this paper. The importance of research on face recognition is fueled by both its scientific challenges and its potential applications. In this contribution, we proposes several approaches to deal with some of the difficulties that one encounters when trying to recognize frontal faces in unconstrained domains and when only one sample per class is available to the learning system. It is possible for an automatic recognition system to compensate for imprecisely localized, partially expression variant faces even when only one single training sample per class is available. Finally, we have shown that the results of an appearance-based approach totally depend on the differences that exist between the facial expressions displayed on the learning and testing images.

引用

页码：5059 / 5062

页数：4

共 50 条

[21] A Novel Face Recognition Method Using Vector Projection
Hu, Changhui
Lu, Xiaobo
Du, Yijun
[J]. BIOMETRIC RECOGNITION (CCBR 2014), 2014, 8833 : 61 - 69
[22] Face recognition using the nearest feature line method
Li, SZ
Lu, JW
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 1999, 10 (02): : 439 - 443
[23] Face recognition using vector quantization histogram method
Kotani, K
Qiu, C
Ohmi, T
[J]. 2002 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS, 2002, : 105 - 108
[24] Face Recognition Using a New Feature Selection Method
Xiao Di
Lin Jinguo
[J]. PROCEEDINGS OF THE 27TH CHINESE CONTROL CONFERENCE, VOL 4, 2008, : 514 - 517
[25] Face recognition using orthonormal discriminant subspace method
Sun, DR
Wu, LN
[J]. ACTIVE MEDIA TECHNOLOGY, 2003, : 314 - 319
[26] EfficientFeature Extraction using Hybrid Face Recognition Method
Rajawat, Aparna
Pandey, Mahendra Kumar
[J]. 2017 7TH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORK TECHNOLOGIES (CSNT), 2017, : 158 - 162
[27] Face alignment by learning from small real datasets and large synthetic datasets
Gao, Haoqi
Ogawara, Koichi
[J]. 2022 ASIA CONFERENCE ON ALGORITHMS, COMPUTING AND MACHINE LEARNING (CACML 2022), 2022, : 397 - 402
[28] An Efficient Face Recognition Method using Contourlet Transform
Biswas, Suparna
Sil, Jaya
[J]. 2015 IEEE INTERNATIONAL CONFERENCE ON RESEARCH IN COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (ICRCICN), 2015, : 27 - 31
[29] Face Recognition Using Consistency Method and Its Variants
Li, Kai
Yang, Nan
Ye, Xiuchen
[J]. ROUGH SET AND KNOWLEDGE TECHNOLOGY (RSKT), 2010, 6401 : 447 - 452
[30] Face recognition using the mixture-of-eigenfaces method
Kim, HC
Kim, D
Bang, SY
[J]. PATTERN RECOGNITION LETTERS, 2002, 23 (13) : 1549 - 1558

← 1 2 3 4 5 →