Hardware Implementation of Cancellable Biometric Systems

elazm, Lamiaa A. Abou; Ibrahim, Sameh A.; Egila, Mohamed G.; Shawkey, Heba; Elsaid, Mohamed; El‐Shafai, Walid; El‐Samie, Fathi E. Abd

doi:10.1109/i-smac49090.2020.9243390

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…The FNC2002 fingerprint dataset contains 800 images that have been acquired with an optical sensor with 500 dots per inch resolution and a size of 388 × 374. CASIA-IrisV3 dataset contains a total of 22035 iris images for more than 700 subjects [22][23][24][25][26][27][28]. All iris images are 8-bit grey-level JPEG files, collected under near-infrared illumination.…”

Section: Tested Datasetsmentioning

confidence: 99%

Cancelable template generation based on quantization concepts

2024

Opto-Electronics Review

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The idea of cancelable biometrics is widely used nowadays for user authentication. It is based on encrypted or intentionally-distorted templates. These templates can be used for user verification, while keeping the original user biometrics safe. Multiple biometric traits can be used to enhance the security level. These traits can be merged together for cancelable template generation. In this paper, a new system for cancelable template generation is presented depending on discrete cosine transform (DCT) merging and joint photographic experts group (JPEG) compression concepts. The DCT has an energy compaction property. The low-frequency quartile in the DCT domain maintains most of the image energy. Hence, the first quartile from each of the four biometrics for the same user is kept and other quartiles are removed. All kept coefficients from the four biometric images are concatenated to formulate a single template. The JPEG compression of this single template with a high compression ratio induces some intended distortion in the template. Hence, it can be used as a cancelable template for the user acquired from his four biometric traits. It can be changed according to the arrangement of biometric quartiles and the compression ratio used. The proposed system has been tested through merging of face, palmprint, iris, and fingerprint images. It achieves a high user verification accuracy of up to 100%. It is also robust in the presence of noise.

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