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2019 | OriginalPaper | Chapter

The Vending Shopper Science Lab: Deep Learning for Consumer Research

Authors : Fioravante Allegrino, Patrizia Gabellini, Luigi Di Bello, Marco Contigiani, Valerio Placidi

Published in: New Trends in Image Analysis and Processing – ICIAP 2019

Publisher: Springer International Publishing

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Abstract

To understand human behavior, a fundamental aspect is the analysis of the face and movement. This aspect is particularly important in the context of sales, where to know the shopper also means to guide purchases. A major challenge for vending environment is to predict the shopper behavior, with the aim to influence and increase purchases. In this ambit, vending machine industry is actually an interesting and growing data-driven marketing area of research. In this context, the aim of this paper is to propose an innovative architecture that is able to integrate face and movement understanding in a common strategy for real time consumer modeling. The vending machine and the decision support system process multimedia data to smartly respond with dynamic pricing and product proposal to the particular shopper which is in front of a vending machine. The aim is to build an intelligent vending machine which in real time is able to suitably propose products to a labelled shopper. The results come from real environments vending lab with 30 locations and about 1 million consumers in Italy, and have the aim to demonstrate the good performances and high efficiency of our solution in recognizing the age and the gender of consumer and different interactions with the vending machine.

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Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 11(3), 647–657 (2010)CrossRef

Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)

Chen, X., Yang, J., Zhang, D., Liang, J.: Complete large margin linear discriminant analysis using mathematical programming approach. Pattern Recogn. 46(6), 1579–1594 (2013)CrossRef

Fan, Z., Xu, Y., Zhang, D.: Local linear discriminant analysis framework using sample neighbors. IEEE Trans. Neural Netw. 22(7), 1119–1132 (2011)CrossRef

He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)CrossRef

Jun, B., Kim, D.: Robust face detection using local gradient patterns and evidence accumulation. Pattern Recogn. 45(9), 3304–3316 (2012)CrossRef

Kautkar, S.N., Atkinson, G.A., Smith, M.L.: Face recognition in 2D and 2.5D using ridgelets and photometric stereo. Pattern Recogn. 45(9), 3317–3327 (2012)CrossRef

Kim, S.-W.: On using a dissimilarity representation method to solve the small sample size problem for face recognition. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 1174–1185. Springer, Heidelberg (2006). https://doi.org/10.1007/11864349_107CrossRef

Kyperountas, M., Tefas, A., Pitas, I.: Weighted piecewise LDA for solving the small sample size problem in face verification. IEEE Trans. Neural Netw. 18(2), 506–519 (2007)CrossRef

10.

Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015)

11.

Li, L., Liu, S., Peng, Y., Sun, Z.: Overview of principal component analysis algorithm. Optik-Int. J. Light Electron Optics 127(9), 3935–3944 (2016)CrossRef

12.

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_20CrossRef

13.

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)

14.

Lu, J., Yuan, X., Yahagi, T.: A method of face recognition based on fuzzy c-means clustering and associated sub-NNs. IEEE Trans. Neural Netw. 18(1), 150–160 (2007)CrossRef

15.

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_17CrossRef

16.

Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in neural information processing systems, pp. 849–856 (2002)

17.

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, pp. V009T07A061–V009T07A061. American Society of Mechanical Engineers (2015)

18.

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_36CrossRef

19.

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

20.

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)

21.

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

22.

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)

23.

Pierdicca, R., Malinverni, E., Piccinini, F., Paolanti, M., Felicetti, A., Zingaretti, P.: Deep convolutional neural network for automatic detection of damaged photovoltaic cells. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42(2) (2018)

24.

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_30CrossRef

25.

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

26.

Pishchulin, L., Gass, T., Dreuw, P., Ney, H.: Image warping for face recognition: from local optimality towards global optimization. Pattern Recogn. 45(9), 3131–3140 (2012)CrossRef

27.

Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

28.

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)

29.

Wang, J., You, J., Li, Q., Xu, Y.: Orthogonal discriminant vector for face recognition across pose. Pattern Recogn. 45(12), 4069–4079 (2012)CrossRef

30.

Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, pp. 456–463. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63460-6_150CrossRef

31.

Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRef

32.

Xu, Y., Zhu, Q., Fan, Z., Qiu, M., Chen, Y., Liu, H.: Coarse to fine k nearest neighbor classifier. Pattern Recogn. Lett. 34(9), 980–986 (2013)CrossRef

33.

Xu, Y., Zhu, X., Li, Z., Liu, G., Lu, Y., Liu, H.: Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recogn. 46(4), 1151–1158 (2013)CrossRef

34.

Zhang, D.: Advanced pattern recognition technologies with applications to biometrics, IGI Global (2009)

35.

Zou, C., Sun, N., Ji, Z., Zhao, L.: 2DCCA: a novel method for small sample size face recognition. In: IEEE Workshop on Applications of Computer Vision 2007, WACV 2007, pp. 43–43. IEEE (2007)

Title: The Vending Shopper Science Lab: Deep Learning for Consumer Research
Authors: Fioravante Allegrino
Patrizia Gabellini
Luigi Di Bello
Marco Contigiani
Valerio Placidi
Publisher: Springer International Publishing
Book: New Trends in Image Analysis and Processing – ICIAP 2019
Print ISBN: 978-3-030-30753-0

Electronic ISBN: 978-3-030-30754-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30754-7_31

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