Analysis of CNN Architectures for Pose Estimation of Noisy 3-D Face Images

Randy Pangestu Kuswana, Faqih Akhmad, Benyamin Kusumoputro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Convolutional neural networks (CNN) has been used in various applications, especially in computer vision field, due to its superiority compare with that of conventional artificial neural networks. In this paper, CNN is developed as head pose estimator for noisy three dimensional face images and analyzed the recognition accuracy for different architectural architecture of the networks, especially on the feature extraction part. Four different amount of layers are experimented, which resulting different input neurons to the estimator part. Experimental results show that the CNN could estimate the head pose with high enough recognition accuracy, and the CNN with 3 feature extraction layers could obtain the highest accuracy of 81.31% for normal face images.

Original languageEnglish
Title of host publication2019 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138732
DOIs
Publication statusPublished - Oct 2019
Event2nd International Conference on Signal Processing and Information Security, ICSPIS 2019 - Dubai, United Arab Emirates
Duration: 30 Oct 201931 Oct 2019

Publication series

Name2019 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019

Conference

Conference2nd International Conference on Signal Processing and Information Security, ICSPIS 2019
CountryUnited Arab Emirates
CityDubai
Period30/10/1931/10/19

Keywords

  • convolutional neural network
  • deep learning
  • head pose estimation
  • image processing

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