Advanced variations of two-dimensional principal component analysis for face recognition
| Autor: | Xiao Chen, Yunfeng Cai, Zhigang Jia, Dunwei Gong, Meixiang Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Generalization business.industry Cognitive Neuroscience Deep learning Pattern recognition 02 engineering and technology Measure (mathematics) Linear subspace Facial recognition system Computer Science Applications Weighting 020901 industrial engineering & automation Artificial Intelligence Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Quaternion business Projection (set theory) |
| Zdroj: | Neurocomputing. 452:653-664 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2020.08.083 |
| Popis: | The two-dimensional principal component analysis (2DPCA) has been one of the basic methods of developing artificial intelligent algorithms. To increase the feasibility, we propose a new general ridge regression model for 2DPCA and variations, with extracting low dimensional features under two projection subspaces. A new relaxed 2DPCA under the quaternion framework is proposed to utilize the label (if known) and color information to compute the essential features of generalization ability with optimization algorithms. The 2DPCA-based approaches for face recognition are also improved by weighting each principle component a scatter measure, which increases efficiently the rate of face recognition. In numerical experiments on well-known standard databases, the R2DPCA approach has high generalization ability and achieves a higher recognition rate than the state-of-the-art 2DPCA-like methods, and has better performance than the basic deep learning methods such as CNNs, DBNs, and DNNs in the small-sample case. |
| Databáze: | OpenAIRE |
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