期刊
NEUROCOMPUTING
卷 393, 期 -, 页码 214-222出版社
ELSEVIER
DOI: 10.1016/j.neucom.2019.01.117
关键词
Dimension reduction; Face recognition; Feature extraction; Kernel discriminant analysis; Probabilistic neural network
资金
- National Natural Science Foundation of China [61662090, 71461027, 71471158]
- science and technology project of Guizhou [[2017]1207]
- training program of high level innovative talents of Guizhou [[2017]3]
- Guizhou province natural science foundation in China [[2014]295, KY[2016]018]
- Zhunyi innovative talent team [(2015)38]
- Science and technology talent training object of Guizhou province outstanding youth [[2015]06]
- Guizhou science and technology cooperation plan [[2016]7028]
- Project of teaching quality and teaching reform of higher education in Guizhou province [[2015]337]
- Zunyi 15851 talents elite project funding
- Innovative talent team in Guizhou Province [[2016]5619]
This paper proposes a hybrid approach to face recognition based on a combination of probabilistic neural networks (PNNs) and improved kernel linear discriminant analysis (IKLDA). The dimensions of a sample's features are first of all reduced, whilst retaining its relevant information, A PNN method is then adopted to solve face recognition problems. The proposed IKLDA+PNN method not only improves the overall computing efficiency, but also its precision. Face recognition experiments conducted on the ORL, YALE and AR datasets, which contain a wide variety of facial expressions, facial details, and degrees of scale, were used to validate the feasibility of the IKLDA+PNN method. The results showed that it can obtain an average recognition accuracy of 97.22%, 83.8% and 99.12%, across the three datasets, respectively. (C) 2019 Elsevier B.V. All rights reserved.
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