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.
Source code and trained models are available at github.com/igorcrexito/skin detector.
References
Arsalan, M., Kim, D., Owais, M., Park, K.: Or-skip-net: outer residual skip network for skin segmentation in non-ideal situations. Expert Syst. Appl. 141 (2020)
Baldissera, D., Nanni, L., Brahnam, S., Lumini, A.: Postprocessing for skin detection. J. Imaging 7 (2021)
Bastos, I.L.O., Angelo, M.F., Loula, A.C.: Recognition of static gestures applied to Brazilian sign language (libras). In: 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 305–312 (2015)
Bastos, I.L.O., Melo, V.H.C., Schwartz, W.R.: Bubblenet: a disperse recurrent structure to recognize activities. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2216–2220 (2020)
Bastos, I.L.O., Soares, L.R., Schwartz, W.R.: Pyramidal Zernike over time: a spatiotemporal feature descriptor based on Zernike moments. In: Mendoza, M., Velastín, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 77–85. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75193-1_10
Casati, J., Moraes, D., Rodrigues, E.: SFA: a human skin image database based on feret and AR facial images. In: IX Workshop de Visão Computacional (2013)
Chen, L., Zhou, J., Liu, Z., Chen, W., Xiong, G.: A skin detector based on neural network. In: IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions, vol. 1, pp. 615–619 (2002)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Dawod, A., Abdullah, J., Alam, M.: Adaptive skin color model for hand segmentation. In: 2010 International Conference on Computer Applications and Industrial Electronics, pp. 486–489 (12 2010)
Dhantre, P., Prasad, R., Saurabh, P., Verma, B.: A hybrid approach for human skin detection. In: 2017 7th International Conference on Communication Systems and Network Technologies (CSNT), pp. 142–146 (2017)
Hajiarbabi, M., Agah, A.: Human skin color detection using neural networks. J. Intell. Syst. 24(4), 425–436 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (June 2016)
He, Y., et al.: Semi-supervised skin detection by network with mutual guidance. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)
Jones, M., Rehg, J.: Statistical color models with application to skin detection. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol. 1, pp. 274–280 (1999)
Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection. In: IEEE EUROCON 2003. Computer as a Tool, vol. 2, pp. 144–148 (2003)
Li, L., Jamieson, K.G., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Efficient hyperparameter optimization and infinitely many armed bandits. CoRR abs/1603.06560 (2016)
Liu, L., Mou, L., Zhu, X., Mandal, M.: Skin lesion segmentation based on improved u-net. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pp. 1–4 (2019)
Micilotta, A., Bowden, R.: View-based location and tracking of body parts for visual interaction. In: BMVC (2004)
Minhas, K., et al.: Accurate pixel-wise skin segmentation using shallow fully convolutional neural network. IEEE Access 8, 156314–156327 (2020)
Peer, P., Solina, F.: An automatic human face detection method. In: Proceedings of the 4th Computer Vision Winter Workshop (CVWW 1999) (December 1999)
Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)
Rahmat, R., Chairunnisa, T., Gunawan, D., Sitompul, O.: Skin color segmentation using multi-color space threshold. In: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS) (August 2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Senior, A., Hsu, R.L., Mottaleb, M.A., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)
Sigal, L., Sclaroff, S., Athitsos, V.: Skin color-based video segmentation under time-varying illumination. Trans. Patt. Anal. Mach. Intell. 26(7), 862–877 (2004)
Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (June 2015)
Tan, W., Chan, C., Yogarajah, P., Condell, J.: A fusion approach for efficient human skin detection. IEEE Trans. Ind. Inform. 8(1), 138–147 (2012)
Tarasiewicz, T., Nalepa, J., Kawulok, M.: Skinny: a lightweight u-net for skin detection and segmentation. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2386–2390 (2020)
Uemori, T., Ito, A., Moriuchi, Y., Gatto, A., Murayama, J.: Skin-based identification from multispectral image data using CNNS. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
Wenjun, T., Gaoyang, D., Han, S., Ziyi, F.: Gesture segmentation based on YCb’Cr’ color space ellipse fitting skin color modeling. In: 2012 24th Chinese Control and Decision Conference (CCDC), pp. 1905–1908 (2012)
Wu, H., Pan, J., Li, Z., Wen, Z., Qin, J.: Automated skin lesion segmentation via an adaptive dual attention module. Trans. Med. Imaging 40(1), 357–370 (2021)
Wu, Q., Cai, R., Fan, L., Ruan, C., Leng, G.: Skin detection using color processing mechanism inspired by the visual system. In: IET Conference on Image Processing (IPR 2012), pp. 1–5 (January 2012)
Yang, J., Sun, X., Liang, J., Rosin, P.L.: Clinical skin lesion diagnosis using representations inspired by dermatologist criteria. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320–4328 (2018)
Zuo, H., Fah, H., Blasch, E., Ling, H.: Combining convolutional and recurrent neural networks for human skin detection. Signal Process. Lett. 24(3), 289–293 (2017)
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).
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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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