دورية أكاديمية
Application of improved virtual sample and sparse representation in face recognition
| العنوان: | Application of improved virtual sample and sparse representation in face recognition |
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| المؤلفون: | Yongjun Zhang, Zewei Wang, Xuexue Zhang, Zhongwei Cui, Bob Zhang, Jinrong Cui, Lamin L. Janneh |
| المصدر: | CAAI Transactions on Intelligence Technology, Vol 8, Iss 4, Pp 1391-1402 (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computational linguistics. Natural language processing LCC:Computer software |
| مصطلحات موضوعية: | Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765 |
| الوصف: | Abstract Sparse representation plays an important role in the research of face recognition. As a deformable sample classification task, face recognition is often used to test the performance of classification algorithms. In face recognition, differences in expression, angle, posture, and lighting conditions have become key factors that affect recognition accuracy. Essentially, there may be significant differences between different image samples of the same face, which makes image classification very difficult. Therefore, how to build a robust virtual image representation becomes a vital issue. To solve the above problems, this paper proposes a novel image classification algorithm. First, to better retain the global features and contour information of the original sample, the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample, thus generating a virtual sample. Second, by the principle of sparse representation, the linear expression coefficients of the original sample and the virtual sample can be calculated, respectively. After obtaining these two types of coefficients, calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test sample. These two distances are converted into distance scores. Finally, a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual image. The fused score will determine the final classification result. The experimental results show that the proposed method outperforms other typical sparse representation classification methods. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2468-2322 |
| Relation: | https://doaj.org/toc/2468-2322 |
| DOI: | 10.1049/cit2.12115 |
| URL الوصول: | https://doaj.org/article/8d2fad96b6534954a0f779212d16408f |
| رقم الأكسشن: | edsdoj.8d2fad96b6534954a0f779212d16408f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24682322 |
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| DOI: | 10.1049/cit2.12115 |