Personal Identification by Integrating a Number of Features from Iris and Periocular Region Using AdaBoost

• Item type: Journal(1)
• 公開日: 2018-07-15
• タイトル: Personal Identification by Integrating a Number of Features from Iris and Periocular Region Using AdaBoost
• タイトル / 言語: en
• タイトル: Personal Identification by Integrating a Number of Features from Iris and Periocular Region Using AdaBoost
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] Iris, Periocular region, Personal Identification
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: The University of Electro-Communications
• 著者所属: The University of Electro-Communications
• 著者所属: The University of Electro-Communications
• 著者所属: The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者名: Shintaro, Oishi; Yoshihiro, Shirakawa; Masatsugu, Ichino; Hiroshi, Yoshiura
• 著者名(英): Shintaro, Oishi; Yoshihiro, Shirakawa; Masatsugu, Ichino; Hiroshi, Yoshiura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: A personal identification method has been developed for searching for a specific person among other people that fuses iris and periocular features using AdaBoost. It effectively integrates scores for many features using an AdaBoost configuration in which feature selection corresponds to weak classifier selection. We found three interesting facts of evaluation. First, evaluation using up to eight features showed that identification accuracy increased with the number of features used. The lowest equal error rate (EER) was 1.3% when eight features were used, and the highest identification rate was 94.1% when eight features were used. Second, the advantage of the proposed method over a weighted sum method increased with the number of features used. The difference in EER was 1.1% when eight features were used due to the generation of a nonlinear decision boundary, and the difference in the identification rate was 1.8% when eight features were used, again due to the generation of a nonlinear decision boundary. Finally, using an effective combination of information from both eyes further improved the accuracy (the difference in EER between the four-feature case and the eight-feature case was 0.7%, and the difference in identification rate between the four-feature case and the eight-feature case was 4.6%).; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.26(2018) (online); DOI http://dx.doi.org/10.2197/ipsjjip.26.518; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: A personal identification method has been developed for searching for a specific person among other people that fuses iris and periocular features using AdaBoost. It effectively integrates scores for many features using an AdaBoost configuration in which feature selection corresponds to weak classifier selection. We found three interesting facts of evaluation. First, evaluation using up to eight features showed that identification accuracy increased with the number of features used. The lowest equal error rate (EER) was 1.3% when eight features were used, and the highest identification rate was 94.1% when eight features were used. Second, the advantage of the proposed method over a weighted sum method increased with the number of features used. The difference in EER was 1.1% when eight features were used due to the generation of a nonlinear decision boundary, and the difference in the identification rate was 1.8% when eight features were used, again due to the generation of a nonlinear decision boundary. Finally, using an effective combination of information from both eyes further improved the accuracy (the difference in EER between the four-feature case and the eight-feature case was 0.7%, and the difference in identification rate between the four-feature case and the eight-feature case was 4.6%).; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.26(2018) (online); DOI http://dx.doi.org/10.2197/ipsjjip.26.518; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 59, 号 7, 発行日 2018-07-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764