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International Conference on Image Analysis and Processing

ICIAP 2019: New Trends in Image Analysis and Processing – ICIAP 2019 pp 275–284Cite as

Collecting Retail Data Using a Deep Learning Identification Experience

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11808))

Abstract

The aim of the paper is to present a part of an architecture realized by Huawei, that propose the first Christmas tree endowed with artificial intelligence. Its ability is to identify facial expressions from images acquired by a mobile application and then recognize the sentiment of the subject. So, basing on the prevailing sentiment the tree lights up itself with different special effects. Our task in the project was testing the performances of the neural networks employed in the mobile application for the recognition of facial emotion. We used a convolutional neural networks model-based and created a purposely dedicated dataset of images for testing the recognition performances.

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References

  1. Bekele, M.K., Pierdicca, R., Frontoni, E., Malinverni, E.S., Gain, J.: A survey of augmented, virtual, and mixed reality for cultural heritage. J. Comput. Cult. Heritage (JOCCH) 11(2), 7 (2018)

    Google Scholar 

  2. Cai, G., Xia, B.: Convolutional neural networks for multimedia sentiment analysis. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) NLPCC 2015. LNCS (LNAI), vol. 9362, pp. 159–167. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25207-0_14

    Chapter  Google Scholar 

  3. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  4. Ghimire, D., Jeong, S., Lee, J., Park, S.H.: Facial expression recognition based on local region specific features and support vector machines. Multimedia Tools Appl. 76(6), 7803–7821 (2017)

    Article  Google Scholar 

  5. Ghimire, D., Lee, J.: Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machines. Sensors 13(6), 7714–7734 (2013)

    Article  Google Scholar 

  6. Happy, S., George, A., Routray, A.: A real time facial expression classification system using local binary patterns. In: 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), pp. 1–5. IEEE (2012)

    Google Scholar 

  7. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)

  8. Ko, B.: A brief review of facial emotion recognition based on visual information. Sensors 18(2), 401 (2018)

    Article  Google Scholar 

  9. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  10. Levi, G., Hassner, T.: Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 503–510. ACM (2015)

    Google Scholar 

  11. Liciotti, D., Paolanti, M., Frontoni, E., Zingaretti, P.: People detection and tracking from an RGB-D camera in top-view configuration: review of challenges and applications. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 207–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70742-6_20

    Chapter  Google Scholar 

  12. Liciotti, D., Paolanti, M., Pietrini, R., Frontoni, E., Zingaretti, P.: Convolutional networks for semantic heads segmentation using top-view depth data in crowded environment. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1384–1389. IEEE (2018)

    Google Scholar 

  13. Naspetti, S., Pierdicca, R., Mandolesi, S., Paolanti, M., Frontoni, E., Zanoli, R.: Automatic analysis of eye-tracking data for augmented reality applications: a prospective outlook. In: De Paolis, L.T., Mongelli, A. (eds.) AVR 2016. LNCS, vol. 9769, pp. 217–230. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40651-0_17

    Chapter  Google Scholar 

  14. Paolanti, M., Frontoni, E., Mancini, A., Pierdicca, R., Zingaretti, P.: Automatic classification for anti mixup events in advanced manufacturing system. In: ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, p. V009T07A061. American Society of Mechanical Engineers (2015)

    Google Scholar 

  15. Paolanti, M., Kaiser, C., Schallner, R., Frontoni, E., Zingaretti, P.: Visual and textual sentiment analysis of brand-related social media pictures using deep convolutional neural networks. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 402–413. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68560-1_36

    Chapter  Google Scholar 

  16. Paolanti, M., Liciotti, D., Pietrini, R., Mancini, A., Frontoni, E.: Modelling and forecasting customer navigation in intelligent retail environments. J. Intell. Robot. Syst. 91(2), 165–180 (2018)

    Article  Google Scholar 

  17. Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6. IEEE (2018)

    Google Scholar 

  18. Paolanti, M., Romeo, L., Martini, M., Mancini, A., Frontoni, E., Zingaretti, P.: Robotic retail surveying by deep learning visual and textual data. Robot. Auton. Syst. 118, 179–188 (2019)

    Article  Google Scholar 

  19. Paolanti, M., Sturari, M., Mancini, A., Zingaretti, P., Frontoni, E.: Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning. In: 2017 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2017)

    Google Scholar 

  20. Pierdicca, R., Malinverni, E., Piccinini, F., Paolanti, M., Felicetti, A., Zingaretti, P.: Deep convolutional neural network for automatic detection of damaged photovoltaic cells. In: International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 42, no. 2 (2018)

    Google Scholar 

  21. Pierdicca, R., Frontoni, E., Pollini, R., Trani, M., Verdini, L.: The use of augmented reality glasses for the application in Industry 4.0. In: De Paolis, L.T., Bourdot, P., Mongelli, A. (eds.) AVR 2017. LNCS, vol. 10324, pp. 389–401. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60922-5_30

    Chapter  Google Scholar 

  22. Pierdicca, R., Paolanti, M., Frontoni, E.: eTourism: ICT and its role for tourism management. J. Hosp. Tour. Technol. 10(1), 90–106 (2019)

    Google Scholar 

  23. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  24. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  25. Sturari, M., Paolanti, M., Frontoni, E., Mancini, A., Zingaretti, P.: Robotic platform for deep change detection for rail safety and security. In: 2017 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2017)

    Google Scholar 

  26. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. JEF Srl, Civitanova Marche, Italy

    Salvatore La Porta & Fabrizio Marconi

  2. Consumer Business Group Italia, Huawei, Milan, Italy

    Isabella Lazzini

Authors
  1. Salvatore La Porta
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  2. Fabrizio Marconi
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  3. Isabella Lazzini
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Corresponding author

Correspondence to Salvatore La Porta .

Editor information

Editors and Affiliations

  1. University of Verona, Verona, Italy

    Marco Cristani

  2. University of Parma, Parma, Italy

    Andrea Prati

  3. Fondazione Bruno Kessler, Povo, Italy

    Oswald Lanz

  4. Fondazione Bruno Kessler, Povo, Italy

    Stefano Messelodi

  5. University of Trento, Povo, Italy

    Nicu Sebe

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Cite this paper

La Porta, S., Marconi, F., Lazzini, I. (2019). Collecting Retail Data Using a Deep Learning Identification Experience. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_28

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  • DOI: https://doi.org/10.1007/978-3-030-30754-7_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30753-0

  • Online ISBN: 978-3-030-30754-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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