MATLAB Implementation of Face Identification using Principal Component Analysis

被引：0
|
作者：
Mahmood, Nasrul Humaimi [1 ]
Ariffin, Ismail [1 ]
Omar, Camallil [1 ]
Jaafar, Nur Sufiah [1 ]
机构：
[1] Univ Teknol Malaysia, Biomed Instrumentat & Elect Res Grp, Dept Elect, Fac Elect Engn, Utm Johor Bahru 81310, Malaysia
来源：
MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷
关键词：
PCA algorithm; Face Identification; Webcam; Biometrics; MATLAB;
D O I：
10.4028/www.scientific.net/AMR.433-440.5402
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Face is the greatest superior biometric as the face has a complex, multidimensional and meaningful identity compared from one person to another. Face identification is executed by comparing the characteristics of the face (test image) with those of known individual images in the database. This paper describes the used of the Principal Component Analysis (PCA) algorithm for human face identification based on webcam image. The MATLAB is used as a tool for image processing and analysis. The important decision to identify the person is by the minimum distance of the face images and known face images in face space. From the results, it can be concluded that the work has successfully implemented the PCA algorithm for human face identification through a webcam.
引用
收藏
页码：5402 / 5408
页数：7
相关论文
共 50 条
  • [1] Face Recognition for Criminal Identification: An implementation of principal component analysis for face recognition
    Abdullah, Nurul Azma
    Saidi, Md. Jamri
    Ab Rahman, Nurul Hidayah
    Wen, Chuah Chai
    Hamid, Isredza Rahmi A.
    2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST'17), 2017, 1891
  • [2] Wavelet Decomposition Based Principal Component Analysis for Face Recognition Using MATLAB
    Sharma, Mahesh Kumar
    Sharma, Shashikant
    Leeprechanon, Nopbhorn
    Ranjan, Aashish
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS (ICCS-2015), 2016, 1715
  • [3] The implementation of effective face detection algorithm using principal component analysis
    Kim, IT
    Ra, JH
    Kim, MH
    IC-AI'2001: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS I-III, 2001, : 1408 - 1413
  • [4] Face Recognition Using Principal Component Analysis
    Kaur, Ramandeep
    Himanshi, Er.
    2015 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2015, : 585 - 589
  • [5] Face classification using a Multiresolution Principal Component Analysis
    Brennan, V
    Principe, J
    NEURAL NETWORKS FOR SIGNAL PROCESSING VIII, 1998, : 506 - 515
  • [6] Face Recognition using Euler Principal Component Analysis
    Boon, Yinn Xi
    Ch'ng, Sue Inn
    2015 4TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION ICIEV 15, 2015,
  • [7] Face recognition using improved principal component analysis
    Nara, Y
    Yang, JM
    Suematsu, Y
    MHS2003: PROCEEDINGS OF 2003 INTERNATIONAL SYMPOSIUM ON MICROMECHATRONICS AND HUMAN SCIENCE, 2003, : 77 - 82
  • [8] Face recognition using kernel principal component analysis
    Kim, KI
    Jung, K
    Kim, HJ
    IEEE SIGNAL PROCESSING LETTERS, 2002, 9 (02) : 40 - 42
  • [9] Face recognition using principal component analysis applied to an Egyptian face database
    Ragab, ME
    Darwish, AM
    Abed, EM
    Shaheen, SI
    MULTIPLE APPROACHES TO INTELLIGENT SYSTEMS, PROCEEDINGS, 1999, 1611 : 540 - 549
  • [10] An analysis of face properties by principal component analysis
    Buchala, S
    Davey, N
    Gale, TM
    Frank, RJ
    PERCEPTION, 2005, 34 : 207 - 207
12345