LBP-ferns-based feature extraction for robust facial recognition

June Young Jung, Seung Wook Kim, Cheol Hwan Yoo, Won Jae Park, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Most facial recognition (FR) systems first extract discriminative features from a facial image and then perform classification. This paper proposes a method aimed at representing human facial traits and a low-dimensional feature extraction method using orthogonal linear discriminant analysis (OLDA). The proposed feature relies on a local binary pattern to represent texture information and random ferns to build a structural model. By concatenating its feature vectors, the proposed method achieves a highdimensional descriptor of the input facial image. In general, the feature dimension is highly related to its discriminative ability. However, higher dimensionality is more complex to compute. Thus, dimensionality reduction is essential for practical FR applications. OLDA is employed to reduce the dimension of the extracted features and improve discriminative performance. With a representative FR database, the proposed method demonstrates a higher recognition rate and low computational complexity compared to existing FR methods. In addition, with a facial image database with disguises, the proposed algorithm demonstrates outstanding performance.

Original languageEnglish
Article number7838098
Pages (from-to)446-453
Number of pages8
JournalIEEE Transactions on Consumer Electronics
Issue number4
Publication statusPublished - 2016 Nov

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • Facial recognition
  • feature extraction
  • local binary patterns
  • orthogonal linear discriminant analysis
  • random-ferns

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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