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Embedded Implementation of a Deep Learning Smile Detector

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2018 7th European Workshop on Visual Information Processing (EUVIP)
Subtitle of host publication26-28 November, 2018, Tampere, Finland
PublisherIEEE
ISBN (Electronic)978-1-5386-6897-9
ISBN (Print)978-1-5386-6898-6
DOIs
Publication statusPublished - Nov 2018
Publication typeA4 Article in a conference publication
EventEuropean Workshop on Visual Information Processing -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2471-8963

Conference

ConferenceEuropean Workshop on Visual Information Processing
Period1/01/00 → …

Abstract

In this paper we study the real time deployment of deep learning algorithms in low resource computational environments. As the use case, we compare the accuracy and speed of neural networks for smile detection using different neural network architectures and their system level implementation on NVidia Jetson embedded platform. We also propose an asynchronous multithreading scheme for parallelizing the pipeline. Within this framework, we experimentally compare thirteen widely used network topologies. The experiments show that low complexity architectures can achieve almost equal performance as larger ones, with a fraction of computation required.

Publication forum classification

Field of science, Statistics Finland