Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition

Changxing Ding, Jonghyun Choi, Dacheng Tao, Larry S. Davis

Research output: Contribution to journalArticlepeer-review

270 Citations (Scopus)

Abstract

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract 'Multi-Directional Multi-Level Dual-Cross Patterns' (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Original languageEnglish
Article number7172530
Pages (from-to)518-531
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Volume38
Issue number3
DOIs
Publication statusPublished - 2016 Mar 1

Bibliographical note

Funding Information:
This work is partially supported by Australian Research Council Projects DP-140102164 and FT-130101457. C. Ding is partially supported by China Scholarship Council. J. Choi and L. S. Davis are partially supported by the US National Science foundation (NSF) Grant IIS1262121.

Publisher Copyright:
© 1979-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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