کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4948490 | 1439613 | 2016 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust face recognition via sparse boosting representation
ترجمه فارسی عنوان
به رسمیت شناختن چهره مقاوم از طریق نمایش نمایشی افزایش یافته است
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کلمات کلیدی
تقویت انعطاف پذیر، نمایندگی خطی، تشخیص چهره، و تشخیص خطا،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Recently linear representation provides an effective way for robust face recognition. However, the existing linear representation methods cannot make an adaptive adjustment in responding to the variations on facial image, so the generalization ability of these methods is limited. In this paper, we propose a sparse boosting representation classification (SBRC) for robust face recognition. To improve the effectiveness of representation coding, an error detection machine (EDM) with multiple error detectors (ED) in SBRC, is proposed to detect and remove destroyed features (i.e. pixels) on a testing image. SBRC has three advantages: First, it has good generalization ability, since the EDM can self-adjust the number of ED according to different variations; Second, EDM would boost the sparsity of coding vector; Third, its implementation is simple and efficient as the EDM is based on l2ânorm. In addition, five popular face image databases including AR database, Extended Yale B database, ORL database, FERET database and LFW database were applied to validate the performance of SBRC. The superiority of SBRC is confirmed by comparing it with the state-of-the-art face recognition methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 944-957
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 944-957
نویسندگان
Tao Liu, Jian-Xun Mi, Ying Liu, Chao Li,