An Intelligent Informative Totem Application Based on Deep CNN in Edge Regime

  • Paolo GiammatteoEmail author
  • Giacomo Valente
  • Alessandro D’Ortenzio
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 627)


In this paper we present an application targeting an informative totem, with a discussion about its possible usage and the requirements it needs to satisfy. In this regard, we propose a Machine Learning algorithm, a Convolutional Neural Network, performing computation on images taken from a camera on an edge-computing platform. Performance tests on two different edge processors are reported, respectively for a CPU and a GPU, and a comparison with the principal competitors is provided. Our final goal is to lay the foundation for the application of an informative totem in an edge computing regime, which is able to recognize the age and the gender of the person approaching it in order to give a better presentation of its contents.


Age and gender estimation Convolutional neural networks Edge computing Embedded systems 


  1. 1.
    Di Mascio T, Gennari R, Melonio A, Tarantino L (2014) Engaging New users into design activities: the TERENCE experience with children. In: Smart organizations and smart artifacts, pp 241–250Google Scholar
  2. 2.
    Satyanarayanan M (2017) The emergence of edge-computing. Computer 50(1):30–39CrossRefGoogle Scholar
  3. 3.
    Shi W, Cao J, Zhang Q, Li Y, Xu L (2008) Artificial intelligence techniques: an introduction to their use for modelling environmental systems. Math Comput Simul 78:379–400MathSciNetCrossRefGoogle Scholar
  4. 4.
    Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge-computing: vision and challenges. IEEE Intern Things J 3:637–646CrossRefGoogle Scholar
  5. 5.
    Atallah RR, Kamsin A, Ismail MA, Abdelrahman SA, Zerdoumi S (2018) Face recognition and age estimation implications of changes in facial feature: a critical review study 6:28290–28304Google Scholar
  6. 6.
    Li H, Ota K, Dong M (2018) Learning IoT in edge: deep learning for the internet of things with edge-computing. IEEE Netw 32–1:96–101CrossRefGoogle Scholar
  7. 7.
    Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. In: 28th IEEE conference on computer vision and pattern recognition (CVPR), pp 34–42, IEEE Press, BostonGoogle Scholar
  8. 8.
    Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9:2170–2179CrossRefGoogle Scholar
  9. 9.
  10. 10.
  11. 11.
  12. 12.
    Dehghan A, Ortiz EG, Shu G, Masood SZ (2017) DAGER: deep age, gender and emotion recognition using convolutional neural networks. arXiv:1702.04280
  13. 13.
  14. 14.
  15. 15.
  16. 16.
    Azarmehr R, Laganire R, Lee WS, Xu C, Laroche D (2015) Real-time embedded age and gender classification in unconstrained video. In: 28th IEEE conference on computer vision and pattern recognition (CVPR), pp 57–65, IEEE Press, BostonGoogle Scholar
  17. 17.
    Chen ATY, Biglari-Abhari M, Wang KIK, Bouzerdoum A, Tivive FHC (2016) Hardware/software co-design for a gender recognition embedded system. In: International conference on industrial, engineering and other applications of applied intelligent systems (IEA/AIE), pp 541–552, MoriokaGoogle Scholar
  18. 18.
    Irick K, DeBole M, Narayanan V, Sharma R, Moon H, Mummareddy S (2007) A unifiedstreaming architecture for real-time face detection and gender classification. In: International conference on field programmable logic and applications, pp 267272. IEEE Press, New YorkGoogle Scholar
  19. 19.
    Giammatteo P, Fiordigigli FV, Pomante L, Di Mascio T, Caruso F (2019) Age & gender classifier for edge computing. In: 2019 8th mediterranean conference on embedded computing (MECO), IEEE Press, BudvaGoogle Scholar
  20. 20.
  21. 21.
    Lemley J, Abdul-Wahid S, Banik D, Andonie R (2016) Comparison of recent machine learning techniques for gender recognitionfrom facial images. In: 27th modern artificial intelligence and cognitive science conference (MAICS), pp 97–102, DaytonGoogle Scholar
  22. 22.
    Meloni P, Capotondi A, Deriu G, Brian M, Conti F, Rossi D, Raffo L, Benini L (2018) NEURAghe: exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on Zynq SoCs. ACM Trans Reconfigurable Technol Syst 11:18:1–18:22Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Paolo Giammatteo
    • 1
    Email author
  • Giacomo Valente
    • 1
  • Alessandro D’Ortenzio
    • 2
  1. 1.Centro di Eccellenza DEWSUniversitá degli Studi dell’AquilaL’AquilaItaly
  2. 2.Dipartimento di Ingegneria e Scienze dell’Informazione e MatematicaUniversitá degli Studi dell’AquilaL’AquilaItaly

Personalised recommendations