Joint Subspace and Low-Rank Coding Method for Makeup Face Recognition
| Autor: | Lei Xue, Jiaqun Zhu, Jianwei Lu, Guohua Zhou |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Article Subject
business.industry Computer science General Mathematics General Engineering 020206 networking & telecommunications Pattern recognition 02 engineering and technology Engineering (General). Civil engineering (General) Facial recognition system Constraint (information theory) ComputingMethodologies_PATTERNRECOGNITION Discriminative model Face (geometry) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) QA1-939 020201 artificial intelligence & image processing Artificial intelligence TA1-2040 Projection (set theory) business Subspace topology Mathematics Coding (social sciences) |
| Zdroj: | Mathematical Problems in Engineering, Vol 2021 (2021) |
| ISSN: | 1563-5147 |
| Popis: | Facial makeup significantly changes the perceived appearance of the face and reduces the accuracy of face recognition. To adapt to the application of smart cities, in this study, we introduce a novel joint subspace and low-rank coding method for makeup face recognition. To exploit more discriminative information of face images, we use the feature projection technology to find proper subspace and learn a discriminative dictionary in such subspace. In addition, we use a low-rank constraint in the dictionary learning. Then, we design a joint learning framework and use the iterative optimization strategy to obtain all parameters simultaneously. Experiments on real-world dataset achieve good performance and demonstrate the validity of the proposed method. |
| Databáze: | OpenAIRE |
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