کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
531789 869876 2016 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Uncorrelated multi-set feature learning for color face recognition
ترجمه فارسی عنوان
یادگیری ویژگی چندگانه غیر مجاز برای تشخیص چهره رنگ
کلمات کلیدی
یادگیری ویژگی های چند منظوره، تشخیص چهره رنگ تجزیه و تحلیل طرح ریزی غیرمنتظره آماری چند مجموعه ای، تجزیه و تحلیل طرح ریزی غیرمتعارف چند متغیری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• We propose a multi-set statistical uncorrelated projection analysis approach.
• We define a supervised correlation that is the discriminating correlation.
• We propose a multi-set discriminating uncorrelated projection analysis approach.
• Performance of our approaches is demonstrated on multiple color face databases.

Most existing color face feature extraction methods need to perform color space transformation, and they reduce correlation of color components on the data level that has no direct connection with classification. Some methods extract features from R, G and B components serially with orthogonal constraints on the feature level, yet the serial extraction manner might make discriminabilities of features derived from three components distinctly different. Multi-set feature learning can jointly learn features from multiple sets of data effectively. In this paper, we propose two novel color face recognition approaches, namely multi-set statistical uncorrelated projection analysis (MSUPA) and multi-set discriminating uncorrelated projection analysis (MDUPA), which extract discriminant features from three color components together and simultaneously reduce the global statistical and global discriminating feature-level correlation between color components in a multi-set manner, respectively. Experiments on multiple public color face databases demonstrate that the proposed approaches outperform several related state-of-the-arts.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 60, December 2016, Pages 630–646
نویسندگان
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