A facial expression recognition algorithm with supervised orthogonal locality preserving projection

Bin Song, Doo Kwon Baik

    Research output: Contribution to journalArticlepeer-review

    Abstract

    According to the characteristics of facial expression, this paper presents a facial expression recognition algorithm with supervised orthogonal locality preserving projection (SOLPP) based on combination of Gabor and local binary pattern (LBP) features. Because of the deficiency of traditional Gabor feature extraction method, an innovative feature extraction method based on Gabor local statistic information is proposed. Each Gabor wavelet representation of an image is divided into some sub-blocks. Then the mean value and standard deviation in each sub-block are calculated, and the statistics of all Gabor wavelet representations are connected as feature vector. Taking into account the effectiveness of LBP to extract local expression texture, we combine local statistic features of Gabor wavelets with LBP textural features as composite facial expression features. After utilizing SOLPP to reduce the feature dimension of composite features, the facial expression image is classified by nearest neighbor method. Experimental results on JAFFE database, CED-WYU (1.0) database and TFEID database indicate the proposed method has higher recognition rate compared with other methods.

    Original languageEnglish
    Pages (from-to)8495-8504
    Number of pages10
    JournalJournal of Computational and Theoretical Nanoscience
    Volume13
    Issue number11
    DOIs
    Publication statusPublished - 2016

    Keywords

    • Facial expression recognition
    • Gabor
    • Local binary pattern
    • Supervised orthogonal locality preserving projection

    ASJC Scopus subject areas

    • Chemistry(all)
    • Materials Science(all)
    • Condensed Matter Physics
    • Computational Mathematics
    • Electrical and Electronic Engineering

    Fingerprint

    Dive into the research topics of 'A facial expression recognition algorithm with supervised orthogonal locality preserving projection'. Together they form a unique fingerprint.

    Cite this