Research Area: Machine Learning
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.
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Author(s) Name: Essam Abdellatef, Nabil A. Ismail, Salah Eldin S. E. Abd Elrahman, Khalid N. Ismail, Mohamed Rihan & Fathi E. Abd El-Samie
Journal name: The Visual Computer
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Publisher name: Springer
DOI: 10.1007/s00371-019-01715-5
Volume Information: volume 36, pages1097–1109 (2020)
