Studies on the biometric authentication and biometric key generation have been underway for a long time. Fundamentally, there is a disadvantage that it is difficult to obtain uniform biometrics due to noise, and issues of security and privacy are still mentioned, but high user convenience is an advantage that cannot be ignored. Recently, the results of existing researches on biometric key generation show very good results. However, since the algorithms presented in many studies are suited to the specific dataset, applying these algorithms to different datasets makes it difficult to achieve the good results mentioned in the paper. The reason is probably because most datasets are collected in one place with one camera. We wanted to present a key generation method that is not limited to datasets, and we came up with a training-based method for this. In this paper, we propose a method with the convolutional neural network (CNN) and the recurrent neural network (RNN) for cryptographic key generation from face biometrics. CNN is used to extract the feature vector from the face image, and RNN generates the key from the feature vector. In the registration process, the RNN is iteratively trained. Experimental results on the databases show that the proposed approach is effective in the biometric key generation. The results for mixed database also show good performance.
KSP Keywords
Biometric authentication, Biometric key, Convolution neural network(CNN), Cryptographic Key Generation, Face Image, Face biometrics, Feature Vector, Long Time, Recurrent Neural Network(RNN), security and privacy
Copyright Policy
ETRI KSP Copyright Policy
The materials provided on this website are subject to copyrights owned by ETRI and protected by the Copyright Act. Any reproduction, modification, or distribution, in whole or in part, requires the prior explicit approval of ETRI. However, under Article 24.2 of the Copyright Act, the materials may be freely used provided the user complies with the following terms:
The materials to be used must have attached a Korea Open Government License (KOGL) Type 4 symbol, which is similar to CC-BY-NC-ND (Creative Commons Attribution Non-Commercial No Derivatives License). Users are free to use the materials only for non-commercial purposes, provided that original works are properly cited and that no alterations, modifications, or changes to such works is made. This website may contain materials for which ETRI does not hold full copyright or for which ETRI shares copyright in conjunction with other third parties. Without explicit permission, any use of such materials without KOGL indication is strictly prohibited and will constitute an infringement of the copyright of ETRI or of the relevant copyright holders.
J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
If you have any questions or concerns about these terms of use, or if you would like to request permission to use any material on this website, please feel free to contact us
KOGL Type 4:(Source Indication + Commercial Use Prohibition+Change Prohibition)
Contact ETRI, Research Information Service Section
Privacy Policy
ETRI KSP Privacy Policy
ETRI does not collect personal information from external users who access our Knowledge Sharing Platform (KSP). Unathorized automated collection of researcher information from our platform without ETRI's consent is strictly prohibited.
[Researcher Information Disclosure] ETRI publicly shares specific researcher information related to research outcomes, including the researcher's name, department, work email, and work phone number.
※ ETRI does not share employee photographs with external users without the explicit consent of the researcher. If a researcher provides consent, their photograph may be displayed on the KSP.