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DASP: Dual-autoencoder Architecture for Skin Prediction

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Book cover Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13232))

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Abstract

This paper introduces a novel human skin detection approach based on the application of a dual autoencoder architecture, composed of models to detect background and skin zones concomitantly. This method, named Dual-Autoencoder Skin Predictor (DASP), associates the outputs of two autoencoders through a composite loss that minimizes the error between predicted skin/background areas and the groundtruth. More importantly, the composite loss penalizes overlapping zones between autoencoders predictions, leading our approach to better capture fine-grained and complementary information between skin and background. To combine semantic information with the skin color distribution, heavily tackled by handcrafted skin detection methods, our architecture relies on a main input that considers multiple colorspaces. Besides, a secondary input provides a standard skin/background patch vector to the model, granting information regarding their color distribution. Our experiments support the accurate performance of the proposed architecture and highlights the contributions of the composite loss and multiple inputs. For instance, DASP achieves the best and the second best results on Pratheepan and Mutual Guidance datasets, respectively.

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Notes

  1. 1.

    Source code and trained models are available at github.com/igorcrexito/skin detector.

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Acknowledgement

The authors would like to thank the National Council for Scientific and Technological Development – CNPq (Grant 309953/2019-7) and the Minas Gerais Research Foundation – FAPEMIG (Grant PPM-00540-17).

Author information

Authors and Affiliations

  1. Smart Sense Laboratory, UFMG, Belo Horizonte, Brazil

    Igor L. O. Bastos, Victor H. C. Melo & William R. Schwartz

  2. Department of Computer Science, UniCamp, Campinas, Brazil

    Raphael F. Prates

Authors
  1. Igor L. O. Bastos
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  2. Victor H. C. Melo
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  3. Raphael F. Prates
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  4. William R. Schwartz
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Corresponding author

Correspondence to Igor L. O. Bastos .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

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O. Bastos, I.L., C. Melo, V.H., Prates, R.F., Schwartz, W.R. (2022). DASP: Dual-autoencoder Architecture for Skin Prediction. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_36

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  • DOI: https://doi.org/10.1007/978-3-031-06430-2_36

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  • Print ISBN: 978-3-031-06429-6

  • Online ISBN: 978-3-031-06430-2

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