A highly accurate face recognition system using filtering correlation

被引:0
|
作者
Watanabe, Eriko [1 ]
Ishikawa, Sayuri [1 ]
Kodate, Kashiko [1 ]
机构
[1] Japan Womens Univ, Fac Sci, Bunkyo Ku, Tokyo 1128681, Japan
关键词
optical correlator; face recognition; phase-only correlation; filtering correlation; cellular phone;
D O I
10.1007/s10043-007-0255-2
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The authors previously constructed a highly accurate fast face recognition optical correlator (FARCO) [E. Watanabe and K. Kodate: Opt. Rev. 12 (2005) 460], and subsequently developed an improved, super high-speed FARCO (SFARCO), which is able to process several hundred thousand frames per second. The principal advantage of our new system is its wide applicability to any correlation scheme. Three different configurations were proposed, each depending on correlation speed. This paper describes and evaluates a software correlation filter. The face recognition function proved highly accurate, seeing that a low-resolution facial image size (64 x 64 pixels) has been successfully implemented. An operation speed of less than 10 ms was achieved using a personal computer with a central processing unit (CPU) of 3 GHz and 2 GB memory. When we applied the software correlation filter to a high-security cellular phone face recognition system, experiments on 30 female students over a period of three months yielded low error rates: 0% false acceptance rate and 2% false rejection rate. Therefore, the filtering correlation works effectively when applied to low resolution images such as web-based images or faces captured by a monitoring camera. (C) 2007 The Optical Society of Japan.
引用
收藏
页码:255 / 259
页数:5
相关论文
共 50 条
  • [1] A Highly Accurate Face Recognition System Using Filtering Correlation
    Eriko Watanabe
    Sayuri Ishikawa
    Kashiko Kodate
    Optical Review, 2007, 14 : 255 - 259
  • [2] Highly accurate and fast face recognition using near infrared images
    Li, SZ
    Chu, RF
    Ao, M
    Zhang, L
    He, R
    ADVANCES IN BIOMETRICS, PROCEEDINGS, 2006, 3832 : 151 - 158
  • [3] Accurate Face Recognition on Highly Compressed Samples
    Khan, Amir
    Fernandez-Berth, Jorge
    Carmona-Galan, Ricardo
    2022 16TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY & INTERNET-BASED SYSTEMS, SITIS, 2022, : 177 - 183
  • [4] Automatic face region tracking for highly accurate face recognition in unconstrained environments
    Kim, YO
    Paik, J
    Heo, J
    Koschan, A
    Abidi, B
    Abidi, M
    IEEE CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE, PROCEEDINGS, 2003, : 29 - 36
  • [5] ShuffleFaceNet: A Lightweight Face Architecture for Efficient and Highly-Accurate Face Recognition
    Martinez-Diaz, Yoanna
    Mendez-Vazquez, Heydi
    Nicolas-Diaz, Miguel
    Luevano, Luis S.
    Chang, Leonardo
    Gonzalez-Mendoza, Miguel
    2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), 2019, : 2721 - 2728
  • [6] Low cost FPGA-based highly accurate face recognition system using combined wavelets with subspace methods
    Shams, Nasim
    Hosseini, Iraj
    Sadri, Mohammad Sadegh
    Azarnasub, Ehsan
    2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS, 2006, : 2077 - +
  • [7] Face recognition system using accurate and rapid estimation of facial position and scale
    Hirayama, T
    Iwai, Y
    Yachida, M
    AUDIO-AND VIDEO-BASED BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS, 2003, 2688 : 154 - 163
  • [8] Fast and Accurate Face Recognition Using SVM and DCT
    Sisodia, Deepti
    Singh, Lokesh
    Sisodia, Sheetal
    PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2012), 2014, 236 : 1027 - 1038
  • [9] Fast and accurate face recognition system using MORSCMs-LBP on embedded circuits
    Hosny, Khalid M.
    Hamad, Aya Y.
    Elkomy, Osama
    Mohamed, Ehab R.
    PEERJ COMPUTER SCIENCE, 2022, 8
  • [10] Face Recognition Using a Facial Recognition System
    Almurayziq, Tariq S.
    Alazani, Abdullah
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2022, 22 (09): : 280 - 286