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Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

S. Aanjanadevi1,*, S. Aanjankumar2, K. R. Ramela3, V. Palanisamy4

1 Department of Computer Applications, Alagappa University, Karaikudi, 630003, Tamilnadu, India
2 Department of Computer Science and Engineering, Sri Raaja Raajan College of Engineering and Technology, Amaravathipudur, 630301, Tamilnadu, India
3 Department of Electrical and Electronics Engineering, Ultra College of Engineering and Technology, Madurai, 625020, Tamilnadu, India
4 Department of Computer Applications, Alagappa University, Karaikudi, 630003, Tamilnadu, India

* Corresponding Author: S. Aanjanadevi. Email:

Computer Systems Science and Engineering 2023, 45(3), 2351-2362.


Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.


Cite This Article

S. Aanjanadevi, S. Aanjankumar, K. R. Ramela and V. Palanisamy, "Face attribute convolutional neural network system for data security with improved crypto biometrics," Computer Systems Science and Engineering, vol. 45, no.3, pp. 2351–2362, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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