Literature DB >> 10725658

Computer-assisted facial image identification system using a 3-D physiognomic range finder.

M Yoshino1, H Matsuda, S Kubota, K Imaizumi, S Miyasaka.   

Abstract

This system consists of a 3-D physiognomic range finder and a computer-assisted facial image superimposition unit. The 3-D range finder is composed of a detector for measuring facial surface and its control computer. The detector has two sinusoidal grating projection devices and two CCD cameras. The computer-assisted facial image superimposition unit consists of a host computer including a proprietary software, a flat surface color display and a color image scanner for inputting 2-D facial images of a criminal. The 3-D facial shape and texture of a suspect is obtained by using the range finder. To make the comparison between the 3-D facial image and the 2-D facial image, the 3-D facial image is first reproduced on a display of the host computer from a MO disk and then the 2-D facial image is taken with the color image scanner and reproduced on the display. The 3-D facial image is exactly adjusted to match the orientation and size of the 2-D facial image under the fine framework mode, and then the fine framework mode of 3-D facial image is converted to the fine texture image. The shape and positional relationships of facial components between the 3-D and 2-D facial images are examined by the fade-out or wipe image mode. The distance between the selected two points and angle among the selected three points on the 3-D and 2-D facial images are automatically measured for the assessment of anthropometrical data between both images. For evaluating the fit between the anthropometrical points on the 3-D and 2-D facial images, the reciprocal point-to-point difference between both images is compared.

Mesh:

Year:  2000        PMID: 10725658     DOI: 10.1016/s0379-0738(00)00149-3

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  8 in total

1.  Detecting hemifacial asymmetries in emotional expression with three-dimensional computerized image analysis.

Authors:  Michael E R Nicholls; Brooke E Ellis; John G Clement; Mineo Yoshino
Journal:  Proc Biol Sci       Date:  2004-04-07       Impact factor: 5.349

2.  A new atlas for the evaluation of facial features: advantages, limits, and applicability.

Authors:  Stefanie Ritz-Timme; Peter Gabriel; Zuzana Obertovà; Melanie Boguslawski; F Mayer; A Drabik; Pasquale Poppa; Danilo De Angelis; Romina Ciaffi; Benedetta Zanotti; Daniele Gibelli; Cristina Cattaneo
Journal:  Int J Legal Med       Date:  2010-04-06       Impact factor: 2.686

3.  Geometric facial comparisons in speed-check photographs.

Authors:  Ursula Buck; Silvio Naether; Kerstin Kreutz; Michael Thali
Journal:  Int J Legal Med       Date:  2010-10-13       Impact factor: 2.686

4.  Assessment of the accuracy of three-dimensional manual craniofacial reconstruction: a series of 25 controlled cases.

Authors:  Gérald Quatrehomme; Thierry Balaguer; Pascal Staccini; Véronique Alunni-Perret
Journal:  Int J Legal Med       Date:  2007-11       Impact factor: 2.686

5.  Facial recognition and laser surface scan: a pilot study.

Authors:  Niels Lynnerup; Maja-Lisa Clausen; Agnethe May Kristoffersen; Henrik Steglich-Arnholm
Journal:  Forensic Sci Med Pathol       Date:  2009-06-09       Impact factor: 2.007

6.  How Different is Different? Criterion and Sensitivity in Face-Space.

Authors:  Harold Hill; Peter Claes; Michelle Corcoran; Mark Walters; Alan Johnston; John Gerald Clement
Journal:  Front Psychol       Date:  2011-03-23

7.  About Face: Matching Unfamiliar Faces Across Rotations of View and Lighting.

Authors:  Simone Favelle; Harold Hill; Peter Claes
Journal:  Iperception       Date:  2017-11-29

Review 8.  An overview of the latest developments in facial imaging.

Authors:  Carl N Stephan; Jodi M Caple; Pierre Guyomarc'h; Peter Claes
Journal:  Forensic Sci Res       Date:  2018-10-29
  8 in total

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