Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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 241–250
Satyanarayanan M (2017) The emergence of edge-computing. Computer 50(1):30–39
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 78:379–400
Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge-computing: vision and challenges. IEEE Intern Things J 3:637–646
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 6:28290–28304
Li H, Ota K, Dong M (2018) Learning IoT in edge: deep learning for the internet of things with edge-computing. IEEE Netw 32–1:96–101
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, Boston
Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9:2170–2179
Google Vision API https://cloud.google.com/vision/?source=post_page
Amazon Rekognition https://aws.amazon.com/it/rekognition/?source=post_page
Sighthound Recognition API https://www.sighthound.com/products/cloud
Dehghan A, Ortiz EG, Shu G, Masood SZ (2017) DAGER: deep age, gender and emotion recognition using convolutional neural networks. arXiv:1702.04280
AXIS, Demographic Identifier, https://www.axis.com/it-it/products/axis-demographic-identifier
Pyramics Pysense https://pyramics.com/en/products/
Fraunhofer IIS Shore. https://www.iis.fraunhofer.de/en/ff/sse/ils/tech/shore-facedetection.html
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, Boston
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, Morioka
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 York
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, Budva
Nvidia Jetson Nano Developer Kit. https://developer.nvidia.com/embedded/jetson-nano-developer-kit
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, Dayton
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 11:18:1–18:22
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Giammatteo, P., Valente, G., D’Ortenzio, A. (2020). An Intelligent Informative Totem Application Based on Deep CNN in Edge Regime. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2019. Lecture Notes in Electrical Engineering, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-37277-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-37277-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37276-7
Online ISBN: 978-3-030-37277-4
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)