Face Recognition Based on Incremental Predictive Linear Discriminant Analysis

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2013/01/01

タイトル(英語): Face Recognition Based on Incremental Predictive Linear Discriminant Analysis

著者名: I Gede Pasek Suta Wijaya (Electrical Engineering Dept., Engineering Faculty, Mataram University/Computer Science and Electrical Engineering of GSST, Kumamoto University), Keiichi Uchimura (Computer Science and Electrical Engineering of GSST, Kumamoto Univ

著者名(英語): I Gede Pasek Suta Wijaya (Electrical Engineering Dept., Engineering Faculty, Mataram University/Computer Science and Electrical Engineering of GSST, Kumamoto University), Keiichi Uchimura (Computer Science and Electrical Engineering of GSST, Kumamoto University), Gou Koutaki (Computer Science and Electrical Engineering of GSST, Kumamoto University)

キーワード: incremental data，LDA，holistic features，face recognition

要約(英語): This paper present an alternative approach to PDLDA for incremental data which belong to old/known and new classes called as incremental PDLDA (IPDLDA). The IPDLDA not only can overcome the main problem of the conventional LDA in terms of large computational cost for retraining but also can provide almost the same optimum projection matrix (W) as that original LDA for each incremental data. The proposed method can be realized by redefining new formulation for updating the between class scatter (Sb) using constant global mean assignment and simplifying the equation for updating the within class scatter (Sw). These new updating algorithms make the IPDLDA require much less time complexity for retraining the incremental data. In addition, they also make the IPDLDA have almost the same properties as the original one in terms of the power discriminant and scattering matrix. To know the ability of the IPDLDA on features clustering, we implement it for face recognition with the DCT-based holistic features as the dimensional reduction of raw face image. The experimental results show the proposed method provides robust recognition rate and less processing time than that of GSVD-ILDA and SP-ILDA in several challenges databases when the experiments were done by retraining the system using two scenarios: the incremental data belonging to new and old classes.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.133 No.1 （2013） 特集：2012 Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2012)

本誌掲載ページ: 74-83 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/133/1/133_74/_article/-char/ja/