Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28-30 June 2020

Research Article

Key Generation Based on Facial Biometrics

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  • @INPROCEEDINGS{10.4108/eai.28-6-2020.2298074,
        author={Ielaf  Abdul Majjed and Alyaa  Abdul Majeed},
        title={Key Generation Based on Facial Biometrics },
        proceedings={Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28-30 June 2020},
        publisher={EAI},
        proceedings_a={IMDC-SDSP},
        year={2020},
        month={9},
        keywords={mrmr speech arnold cat map boi-key facial image},
        doi={10.4108/eai.28-6-2020.2298074}
    }
    
  • Ielaf Abdul Majjed
    Alyaa Abdul Majeed
    Year: 2020
    Key Generation Based on Facial Biometrics
    IMDC-SDSP
    EAI
    DOI: 10.4108/eai.28-6-2020.2298074
Ielaf Abdul Majjed1,*, Alyaa Abdul Majeed1
  • 1: College of computer sciences and mathematics, University of Mosul
*Contact email: Ie_osamah@uomosul.edu.iq

Abstract

Over the past few years, advances in communication technology have brought large amounts of digital data to ordinary media, which required the development of computer security technologies. In this work, we suggested a new method to generate a biometric key to encrypt data using the properties of the human face, then used this key to encrypting speech messages and hide them inside the colored images. This can be achieved depending on splitting the facial image into two parts (upper and lower parts) and then generated a unique encryption key using Maximum-Relevance Minimum Redundancy (mRMR) feature selection algorithm from the upper part after that encrypted the original speech message using two levels, in the first level we used Arnold cat map to permutation the samples then in the second level used bio-key to encrypting the message and then hide the encrypted speech message in the lower part of the facial image. In order to determine the efficiency of the proposed method, different measures were applied (correlation coefficient, PSNR, MSR, SSIM).