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TECHNICAL PAPERS

Digital Photogrammetry for Facial Recognition

[+] Author and Article Information
L. M. Galantucci, G. Percoco

Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Bari, Italy

R. Ferrandes

Laboratoire Sols, Solides et Strucutres,  Institut National Polytechnique de Grenoble, Grenoble, France

J. Comput. Inf. Sci. Eng 6(4), 390-396 (May 30, 2006) (7 pages) doi:10.1115/1.2356499 History: Received August 09, 2005; Revised May 30, 2006

In this paper, the authors present a biometric low-cost 3D acquisition system, based on a digital photogrammetry technique. The aim of the work is to analyze the suitability of this system for facial recognition purposes. The facial data of a set of 20 people were acquired with the photogrammetric system developed by the authors, and different CAD 3D models were reconstructed for each person. The results are quantified by aligning the models and calculating mean distances and standard deviations between them using two different methods. The former considers the entire face; the latter is based on a few fiducial points of the face.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 4

Image and related tessellated model of one of the subjects

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Figure 5

Mean distances in comparisons between two models of the same person

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Figure 6

Standard deviation in comparisons between two models of the same person

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Figure 7

Mean error (a) and standard deviation (b) in tessellated-tessellated comparison (dark columns) and tessellated-point cloud (light columns) related to the subject “male 9”

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Figure 8

Mean error (a) and standard deviation (b) in tessellated-tessellated comparison (dark columns) and tessellated-point cloud (light columns) related to the subject “female 3”

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Figure 9

Mean error in comparisons between models made up of fiducial points related to the same person

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Figure 10

Mean error in comparisons between models made up of fiducial points related to the subject “male 9”

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Figure 1

Classification of facial recognition algorithms

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Figure 2

Positioning of the cameras. Points A, B, and C correspond to the positions of the digital cameras, inferred during the orientation process.

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Figure 3

The point cloud obtained

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