Human verification using 3D-gray-scale face image

  • Ryutaro Ito
  • Kazuo Nakazawa
  • Masato Nakajima
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


In recent years, many different techniques have been proposed to verify human face. Most of them used gray-scale image and some of them used range image. Because the two types of image include different information, their combination can increase the correct verification rate. The paper propose a human face verification method using a 3D-gray-scale imaging technique. First, we developed an effectiveness procedure for obtaining a 3D-gray-scale face image based on a 2D-gray-scale image and a range image. Then we propose a human face verification technique using the subspace method: to verify whether the observed person is himself, distance is calculated between the face data of the person and the subspace of the registered data. In order to verify the effectiveness of our method, we applyed this method to 18 people. The result of experiment shows effectiveness of the method.


Feature Vector Face Image Human Face Range Image Subspace Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    Makoto Kosugi: “Human-Face Recognition Using Mosaic Pattern and Neural Networks”, IEICE Trans(D-II),J-76-D-II,No.6,pp.1132–1139 (1993)Google Scholar
  2. [2]
    Yoshie Komatsu: “Orientation Invariant Face Extraction and Recognition based on Subspace Method”, TECHNICAL REPORT OF IEICE,PRU95-191,(1996-01)Google Scholar
  3. [3]
    Roberto Brunelli, Tomaso Poggio: “Face Recognition: Features versus Templates”, IEEE,Trans,Patt.Anal.Machine Intell.,Vol.12,No.l0,pp.1042–1052,1993Google Scholar
  4. [4]
    Yong-Qing Cheng,Ke Liu,Jing-Yu Yang: “A NOVEL FEATURE EXTRACTION METHOD FOR IMAGE RECOGNITION BASED ON SIMILAR DISCRIMINANT FUNXTION(SDF)”, Pattern Recognition,Vo1.26,No.l,pp.115–125,1993CrossRefGoogle Scholar
  5. [5]
    David J. Beymer: “Face Recognition Under Varying Pose”, In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, pp.756–761,1994Google Scholar
  6. [6]
    David Beymer, Tomaso Poggio: “Face Recognition From One Example View”, In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, pp.500–507,1995Google Scholar
  7. [7]
    Kenji Terada,Jun'ichi Yamaguchi,Masato Nakajima: “An Automatic Identification of Human Faces using Fiber Grating Vision Sensor”, T.IEE Japan,Vol.113C,No.12,pp.1072–1078, (1993)Google Scholar
  8. [8]
    Shinya Imabeppu: “Individual Identification by Unifying Profile and Front-View Images”, TECHNICAL REPORT OF IEICE,PRU94-148,(1995-03)Google Scholar
  9. [9]
    Hiroshi Murase: “3D Object Recognition from Appearance-Parametric Eigen space Method-”, T.IEE Japan,Vol.J77-D-II,No.ll,pp.2179–2187 (1994)Google Scholar
  10. [10]
    Kazuo Nakazawa: “Development of Compact Vision Sensor by Fusion of 2-D Intensity Image and Sparse Range Image”, T.IEE Japan,Vo1.115,No.3,pp.389–395 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Ryutaro Ito
    • 1
  • Kazuo Nakazawa
    • 1
  • Masato Nakajima
    • 1
  1. 1.Faculty of Science and TechnologyKeio UniversityKouhoku ward Yokohama cityJapan

Personalised recommendations