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Cancelable multi-biometric recognition system based on deep learning.
- Source :
-
Visual Computer . Jun2020, Vol. 36 Issue 6, p1097-1109. 13p. - Publication Year :
- 2020
-
Abstract
- In this paper, we propose a cancelable multi-biometric face recognition method that uses multiple convolutional neural networks (CNNs) to extract deep features from different facial regions. We also propose a new CNN architecture that exploits batch normalization, depth concatenation and a residual learning framework. The proposed method adopts a region-based technique in which face, eyes, nose and mouth regions are detected from the original face images. Multiple CNNs are used to extract deep features from each region, and then, a fusion network combines these features. Moreover, to provide user's privacy and increase the system resistance against spoof attacks, a cancelable biometric technique using bio-convolving encryption is performed on the final facial descriptor. Our experiments on the FERET, LFW and PaSC datasets show excellent and competitive results compared to state-of-the-art methods in terms of recognition accuracy, specificity, precision, recall and fscore. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01782789
- Volume :
- 36
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Visual Computer
- Publication Type :
- Academic Journal
- Accession number :
- 142925590
- Full Text :
- https://doi.org/10.1007/s00371-019-01715-5