Abstract
This article introduces a new cancellable multi-biometric system based on the combination of a selective encryption method and a deep-learning-based fusion technology. The biometric face image is treated with an automatic face segmentation algorithm (Viola-Jones), and the image of the selected eye is XORed with a PRNG (Pseudo Random Number Generator) matrix. The output array is used to create a primary biometric template. This process changes the histogram of the selected eye image. Arnold’s Cat Map is used to superimpose the PRN pixels only on the pixels of the primary image. Arnold’s cat map deformed eyes are encrypted using the Advanced Encryption Standard (AES) to encrypt the biometric data stored in the database. In addition, the AES master key is used for the same person in the identity verification process to verify the biometric identity. It is created from the fingers of the right hand, and the right eye is integrated into this process using deep learning technology. The deep learning fusion process can prevent attacks on the biometric system as a whole. In order to avoid damage to the eye or fingerprint images, the design considers the other eye and fingerprint images.
Highlights
Biometric systems automatically recognize individuals based on unique characteristics or types of characteristics they possess
We introduce the concept of cancellable biometrics, in addition to biometric protection and privacy problems
Statistical tests of the proposed cancellable biometric system are applied on the individual face templates. 4.2 Performance Evaluation
Summary
Biometric systems automatically recognize individuals based on unique characteristics or types of characteristics they possess. It is possible to realize secure dynamic transmission of biometric templates through strong encryption methods such as the Advanced Encryption Standard (AES) [1]. A multibiometric system recognizes people based on a single source of vital information [2] These types of systems are often affected by several problems such as disturbed sensor data, lack of individuality, static representation, circumvention, and lack of comprehensiveness. 3) The system performance in the existence of noise, ROC, and AROC are good This makes the proposed technology more suitable for real-time applications. 4) The proposed technology depends on Viola-Jones algorithm, which is a faster face detection technology It produces good detection results suitable for machine learning applications. 5) The proposed algorithm speeds up the database search process by generating the applicant key and using the key stored in the database to find out if the person is authenticated or not, and in the case of unauthorized persons, the authentication rejects the person
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