Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Sepehr Damavandinejadmonfared"'
Publikováno v:
International Journal of Academic Research. 4:79-83
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a67ee70bb2a9ae96b48e059db1832469
https://doi.org/10.7813/2075-4124.2012/4-5/a.8
https://doi.org/10.7813/2075-4124.2012/4-5/a.8
In this paper, a whole identification system is introduced for finger vein recognition. The proposed algorithm first maps the input data into kernel space, then; two-dimensional principal component analysis (2DPCA) is applied to extract the most valu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::559f56e9a4a7645a21a122d38ba36ba9
https://doi.org/10.1016/b978-0-12-802045-6.00025-9
https://doi.org/10.1016/b978-0-12-802045-6.00025-9
Autor:
A. Abdel-Dayem, Ryo Aita, Samet Akpınar, Ferda Nur Alpaslan, Kyota Aoki, Hamid R. Arabnia, S. Arboleda-Duque, R. Ardekani, Ramazan S. Aygün, Pham The Bao, Robert Beck, Christopher Blay, H. Chen, Haijung Choi, Clarimar José Coelho, Eduardo Tavares Costa, Anderson da Silva Soares, Sepehr Damavandinejadmonfared, Maria Stela Veludo de Paiva, Leonidas Deligiannidis, İmren Dinç, Semih Dinç, Gregory Doerfler, Min Dong, Arezoo Ektesabi, Hany A. Elsalamony, B. Foley, Faouzi Ghorbel, J.B. Gómez-Mendoza, Marco Aurélio Granero, Marco Antônio Gutierrez, M. Hariyama, A. Hematian, Chuen-Min Huang, Nguyen Tuan Hung, M. Ilie, Rowa’a Jamal, J. Johnson, Eui Sun Kang, Ajay Kapoor, A. Karimian, Loay Khalaf, Jin Young Kim, Manbae Kim, Bernd Klässner, Vladimir Kulyukin, Gustavo Teodoro Laureano, Xiangyu Lu, Yide Ma, Saeed Mahmoudpour, Karmel Manaa, P. Marjoram, Mohamed Amine Mezghich, Slim M’Hiri, José Manuel Miranda, Hector A. Montes-Venegas, Oswaldo Morales, Jaime Moreno, Dae Hyuck Park, M. Pashna, Alfredo Petrosino, Marc L. Pusey, Maram Rabee’a, Marcelo Romero, M. Saadatseresht, Maweheb Saidani, Giuseppe Salvi, Jeong Goo Seo, Tamara Seybold, M. Shimoda, Madhav Sigdel, Madhu S. Sigdel, Dominik Spinczyk, Walter Stechele, Iulia Stirb, Hongjun Su, R. Talebi, J.D. Tamayo-Quintero, Ricardo Tejeida, Md. Zia Uddin, Vijay Varadharajan, Keju Wang, Chuangbai Xiao, S. Yazdani, R. Yusof, Hong Zhang, Hongyu Zhao, Huaxia Zhao
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e103c90fca1d547d679897e2944de5d
https://doi.org/10.1016/b978-0-12-802045-6.09986-5
https://doi.org/10.1016/b978-0-12-802045-6.09986-5
Kernel functions have been very useful in data classification for the purpose of identification and verification so far. Applying such mappings first and using some methods on the mapped data such as principal component analysis (PCA) has been proven
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d457eb480ff32a15edac820bc4a1bf28
https://doi.org/10.1016/b978-0-12-802045-6.00029-6
https://doi.org/10.1016/b978-0-12-802045-6.00029-6
In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1820e1740a3d33cc04a3e152224ee63d
Publikováno v:
2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing.
Based on the previous research, Kernel Entropy Component Analysis (KECA) is introduced as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face recognition. In this paper, an algorithm using KECA is proposed to merit fing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff6685072dbeed4b7468525fb9b8f0a5
https://doi.org/10.1109/iccp.2012.6356194
https://doi.org/10.1109/iccp.2012.6356194
Publikováno v:
2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing.
In this paper, a new method for face recognition in video surveillance is proposed. Local mean-based k-nearest centroid neighbour (LMKNCN) is a recently proposed method for classifying data which has been proven to be more appropriate than other clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0ea470227b849290a6fa2d104d6340d
https://doi.org/10.1109/iccp.2012.6356195
https://doi.org/10.1109/iccp.2012.6356195
Publikováno v:
ICDIP
Kernel Entropy Component Analysis (KECA) is a newer method than Kernel Principle Component Analysis (KPCA) for data transformation and dimensionality reduction in case of face recognition. Although in almost all previous researches using KECA are sho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6cd0c862666c532cbb24b7b39b72945
https://doi.org/10.1117/12.956494
https://doi.org/10.1117/12.956494
Autor:
Shahrel Azmin Suandi, Ali Khalili Mobarakeh, Sepehr Damavandinejadmonfared, Bakhtiar Affendi Rosdi
Publikováno v:
Procedia Engineering. :516-521
In this paper, the performance of a variety of different methods of dimensionality reduction on finger vein database is evaluated to determine the most appropriate one in terms of finger vein recognition. Principal Component Analysis using K-nearest
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a73a7e51da6ede985568d80486b1caad