Rotated face recognition by manifold learning with auto-associative neural network

Mizuki Ito, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

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

1 Citation (Scopus)

Abstract

The performance of face recognition is easily affected by appearance variation by face rotation. The proposed method in this research recognizes who is a subject in the query image in which a face is captured from an arbitrary direction. The proposed method employs an auto-associative neural network for learning a manifold which represents principal variation of facial appearance in feature space due to face rotation. Our comparison where four conditions of selecting training samples for manifold learning were adopted implied that rotated third parson faces and its reference frontal face can be applicable for the manifold learning. The results in evaluation experiments with SCface database showed that the highest recognition accuracy at RANK10 is 77.5 %.

Original languageEnglish
Title of host publication2015 Frontiers of Computer Vision, FCV 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479917204
DOIs
Publication statusPublished - May 7 2015
Event2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015 - Mokpo, Korea, Republic of
Duration: Jan 28 2015Jan 30 2015

Publication series

Name2015 Frontiers of Computer Vision, FCV 2015

Other

Other2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015
CountryKorea, Republic of
CityMokpo
Period1/28/151/30/15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Ito, M., Ohyama, W., Wakabayashi, T., & Kimura, F. (2015). Rotated face recognition by manifold learning with auto-associative neural network. In 2015 Frontiers of Computer Vision, FCV 2015 [7103724] (2015 Frontiers of Computer Vision, FCV 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FCV.2015.7103724