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Face reconstruction from image sequences for forensic face comparison

Face reconstruction from image sequences for forensic face comparison

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The authors explore the possibilities of a dense model-free three-dimensional (3D) face reconstruction method, based on image sequences from a single camera, to improve the current state of forensic face comparison. They propose a new model-free 3D reconstruction method for faces, based on the Lambertian reflectance model to estimate the albedo and to refine the 3D shape of the face. This method avoids any form of bias towards face models and is therefore suitable in a forensic face comparison process. The proposed method can reconstruct frontal albedo images, from multiple non-frontal images. Also a dense 3D shape model of the face is reconstructed, which can be used to generate faces under pose. In the authors’ experiments, the proposed method is able to improve the face recognition scores in more than 90% of the cases. Using the likelihood ratio framework, they show for the same experiment that for data initially unsuitable for forensic use, the reconstructions become meaningful in a forensic context in more than 60% of the cases.

References

    1. 1)
      • V. Blanz , T. Vetter .
        1. Blanz, V., Vetter, T.: ‘A morphable model for the synthesis of 3d faces’. Proc. of the 26th Annual Conf. on Computer Graphics and Interactive Techniques, 1999, pp. 187194.
        . Proc. of the 26th Annual Conf. on Computer Graphics and Interactive Techniques , 187 - 194
    2. 2)
    3. 3)
      • Y. Shan , Z. Liu , Z. Zhang .
        3. Shan, Y., Liu, Z., Zhang, Z.: ‘Model-based bundle adjustment with application to face modeling’. IEEE Int. Conf. on Computer Vision, 2001, vol. 2, p. 644.
        . IEEE Int. Conf. on Computer Vision , 644
    4. 4)
      • A.K. Roy-Chowdhury .
        4. Roy-Chowdhury, A.K.: ‘3d face reconstruction from video using a generic model’. Int. Conf. on Multimedia and Expo, 2002, pp. 449452.
        . Int. Conf. on Multimedia and Expo , 449 - 452
    5. 5)
    6. 6)
    7. 7)
      • A.K. Roy-Chowdhury , R. Chellappa , H. Gupta . (2005)
        7. Roy-Chowdhury, A.K., Chellappa, R., Gupta, H.: ‘3D face modeling from monocular video sequences’ (Academic Press, 2005), Ch. 6, pp. 185218.
        .
    8. 8)
      • D. Fidaleo , G.G. Medioni . (2007)
        8. Fidaleo, D., Medioni, G.G.: ‘Model-assisted 3d face reconstruction from video’, in Zhou, S.K., Zhao, W., Tang, X., Gong, S. (Eds.): ‘AMFG’, ser. Lecture Notes in Computer Science (Springer, 2007), vol. 4778, pp. 124138.
        .
    9. 9)
      • U. Park , A.K. Jain . (2007)
        9. Park, U., Jain, A.K.: ‘3d model-based face recognition in video’, in Lee, S.-W., Li, S. (Eds.): ‘Advances in biometrics’, ser. Lecture Notes in Computer Science (Springer Berlin Heidelberg, 2007), vol. 4642, pp. 10851094.
        .
    10. 10)
      • M. Marques , J. Costeira .
        10. Marques, M., Costeira, J.: ‘3d face recognition from multiple images: A shape-from-motion approach’. FG, 2008, pp. 16.
        . FG , 1 - 6
    11. 11)
    12. 12)
      • M. Ishimoto , Y.-W. Chen .
        12. Ishimoto, M., Chen, Y.-W.: ‘Pose-robust face recognition based on 3d shape reconstruction’. Fifth Int. Conf. on Natural Computation, 2009, ICNC'09, 2009, vol. 6, pp. 4043.
        . Fifth Int. Conf. on Natural Computation, 2009, ICNC'09 , 40 - 43
    13. 13)
      • O.C. Hamsici , P.F.U. Gotardo , A.M. Martinez .
        13. Hamsici, O.C., Gotardo, P.F.U., Martinez, A.M.: ‘Learning spatially-smooth mappings in non-rigid structure from motion’. Proc. of the 12th European Conf. on Computer Vision, ECCV'12, 2012, pp. 260273.
        . Proc. of the 12th European Conf. on Computer Vision, ECCV'12 , 260 - 273
    14. 14)
      • L. Spreeuwers .
        14. Spreeuwers, L.: ‘Multi-view passive 3d face acquisition device’. Proc. of the Special Interest Group on Biometrics and Electronic Signatures, ser. Lecture Notes in Informatics (LNI) – Proc., September 2008, vol. P-137, pp. 1324.
        . Proc. of the Special Interest Group on Biometrics and Electronic Signatures, ser. Lecture Notes in Informatics (LNI) – Proc. , 13 - 24
    15. 15)
      • C. Strecha , R. Fransens , L. Van Gool .
        15. Strecha, C., Fransens, R., Van Gool, L.: ‘Combined depth and outlier estimation in multiview stereo’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2006, 2006, vol. 2, pp. 23942401.
        . IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2006 , 2394 - 2401
    16. 16)
      • R. Garg , A. Roussos , L. Agapito .
        16. Garg, R., Roussos, A., Agapito, L.: ‘Dense variational reconstruction of non-rigid surfaces from monocular video’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2013, June 2013, pp. 12721279.
        . IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2013 , 1272 - 1279
    17. 17)
      • A. Delaunoy , M. Pollefeys .
        17. Delaunoy, A., Pollefeys, M.: ‘Photometric bundle adjustment for dense multi-view 3d modeling’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2014, June 2014, pp. 14861493.
        . IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2014 , 1486 - 1493
    18. 18)
      • C. van Dam , L. Spreeuwers , R. Veldhuis .
        18. van Dam, C., Spreeuwers, L., Veldhuis, R.: ‘Landmark-based model-free 3d face shape reconstruction from video sequences’. Proc. of the Int. Conf. of Biometrics Special Interest Group 2013 BIOSIG, September 2013, pp. 265272.
        . Proc. of the Int. Conf. of Biometrics Special Interest Group 2013 BIOSIG , 265 - 272
    19. 19)
      • 19. Cognitec Systems GmbH: ‘FaceVACS SDK 8.8.0’, http://www.cognitec-systems.de, 2013.
        .
    20. 20)
      • D. Meuwly , R.N.J. Veldhuis . (2014)
        20. Meuwly, D., Veldhuis, R.N.J.: ‘Biometrics – developments and potential’, in Allan, Jamieson, Andre, A. Moenssens (Eds.): ‘Wiley encyclopaedia of forensic science’ (John Wiley & Sons Ltd, Chichester, UK, 2014), vol. 1, pp. 18.
        .
    21. 21)
      • R. Veldhuis .
        21. Veldhuis, R.: ‘The relation between the secrecy rate of biometric template protection and biometric recognition performance’. Int. Conf. on Biometrics (ICB), 2015, 2015, vol. 5, pp. 311318.
        . Int. Conf. on Biometrics (ICB), 2015 , 311 - 318
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