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Age and gender-based human face reconstruction from single frontal image

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Abstract

We present an approach for the human face reconstruction from a single frontal image for the use in forensic anthropology when the subject’s age and gender is known. In our approach we build a database of several depth images per each age and gender group pair, marked with facial landmarks. To reconstruct a 3D facial model from an unknown frontal image we search the most similar face in the depth database based on the automatically detected landmarks and assign its depth to the model. In the evaluation part, we compared our approach to a recent automatic convolutional neural network based algorithm and a semi-automatic approach, where landmarks are required to be detected manually. In contrast to other tested approaches our algorithm can estimate all major components, such as eyes, nose and mouth, evenly. Thanks to the external depth database, it can also reconstruct human faces from images with partial facial occlusions and uneven lighting. Additionally, we have found that a single depth image provides a good approximation of the human face and a combination of multiple precomputed depth images has a little impact on the final 3D face reconstruction result. Speed measurements show that our algorithm provides a quick and a fully automatic way to reconstruct a human face from a single frontal image for the use in forensic anthropology.

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Notes

  1. Available at http://fidentis.cz

  2. Available at: https://facegen.com/

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Authors and Affiliations

  1. Department of Visual Computing, Masaryk University, Botanická 68a, 60200, Brno, Czech Republic

    Zuzana Ferková, Marek Žuži & Petr Matula

  2. Department of Anthropology, Masaryk University, Kotlářska 2, 60200, Brno, Czech Republic

    Petra Urbanová & Dominik Černý

Authors
  1. Zuzana Ferková
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  2. Petra Urbanová
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  3. Dominik Černý
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Correspondence to Zuzana Ferková.

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Ferková, Z., Urbanová, P., Černý, D. et al. Age and gender-based human face reconstruction from single frontal image. Multimed Tools Appl 79, 3217–3242 (2020). https://doi.org/10.1007/s11042-018-6869-5

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