PCA filter based covariance descriptor for 2.5D face recognition

Lee Ying Chong, Andrew Beng Jin Teoh, Thian Song Ong

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

1 Citation (Scopus)

Abstract

Region covariance matrix (RCM) as a feature descriptor is shown promising in various object detection and recognition tasks. However, vanilla RCM breaks down in face recognition due to its inadequacy in extracting discriminative features from facial image. In this paper, cascaded Principle Component Analysis (PCA) filter responses that derived from the multi-layer PCA network are leveraged to extract the sufficient discriminative facial feature for RCM construction. The factors that affect the performance of cascaded PCA filter responses in forming RCM for 2.5D face recognition is investigated. To be specific, the influence of patch size and filter numbers of cascaded PCA filter responses to RCM is probed. Besides that, block division is proposed for RCM to further enhance the accuracy performance. Experimental results have demonstrated the efficacy of the proposed approach.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Biometrics Engineering and Application, ICBEA 2017
PublisherAssociation for Computing Machinery
Pages13-20
Number of pages8
ISBN (Electronic)9781450348713
DOIs
Publication statusPublished - 2017 Apr 21
Event2017 International Conference on Biometrics Engineering and Application, ICBEA 2017 - Hong Kong, Hong Kong
Duration: 2017 Apr 212017 Apr 23

Publication series

NameACM International Conference Proceeding Series
VolumePart F128052

Other

Other2017 International Conference on Biometrics Engineering and Application, ICBEA 2017
CountryHong Kong
CityHong Kong
Period17/4/2117/4/23

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Chong, L. Y., Teoh, A. B. J., & Ong, T. S. (2017). PCA filter based covariance descriptor for 2.5D face recognition. In Proceedings of 2017 International Conference on Biometrics Engineering and Application, ICBEA 2017 (pp. 13-20). (ACM International Conference Proceeding Series; Vol. Part F128052). Association for Computing Machinery. https://doi.org/10.1145/3077829.3077832