دوشنبه 10 اردیبهشت 1403
| عنوان مقاله | |
| عنوان مقاله |
Improved Principal Component Analysis and Linear regression classification for face recognition |
| عنوان فارسی مقاله | تجزیه و تحلیل مؤلفه اصلی بهبود یافته و طبقه بندی رگرسیون خطی برای تشخیص چهره |
| مشخصات مقاله انگلیسی | |
| نشریه: Elsevier |
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| سال انتشار |
2018 |
| عنوان مجله |
Signal Processing |
| تعداد صفحات مقاله انگلیسی | 21 |
| رفرنس | دارد |
| تعداد رفرنس | 46 |
| چکیده مقاله | |
| چکیده |
In this paper, an improved principal component analysis (IPCA) is presented for face feature representation. IPCA is mainly designed to extract the useful information from original face images through reducing the dimension of feature vectors. Linear regression classification (LRC) algorithm is employed to treat the face recognition as a linear regression issue. LRC uses the least-square method to decide the class label with the minimum reconstruction error. Experiments are conducted on the Yale B, CMU_PIE and JAFFE databases. The proposed IPCA algorithm and LRC algorithm achieve better recognition results than that of state-of-the-art algorithms. |
| کلمات کلیدی |
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| دانلود | |
