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Journal of Intelligent Systems and Internet of Things
Volume 10 , Issue 1, PP: 21-32 , 2023 | Cite this article as | XML | Html |PDF

Title

An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing

  N. A. Majeed alhammadi 1 * ,   K. Hameed Zaboon 2 ,   A. Abdulhadi Abdullah 3

1  Department of computer sciences, shatt al-arab University college, Al Basrah, 61001, Iraq
    (Nafeaalhamadi@yahoo.com)

2  Department of computer sciences, shatt al-arab University college, Al Basrah, 61001, Iraq
    (Khalid.Hameed842@gmail.com)

3  Department of computer sciences, shatt al-arab University college, Al Basrah, 61001, Iraq
    (ammarabdulhadiabdullah@sa-uc.edu.iq)


Doi   :   https://doi.org/10.54216/JISIoT.100102

Received: March 17, 2023 Revised: June 09, 2023 Accepted: September 05, 2023

Abstract :

The security and privacy fields and multimedia biometrics have been widely used today for personal authentication. Sclera and Palm-print of humans are one of the fastest, accurate, reliable, and secure biometric techniques for identification and verification based on unique features. The majority of the biometric systems are based on the global features, which may lead to weak performance in cases of poor-quality biometric images, therefore, swarm intelligence techniques are used to improve recognition accuracy, reliability, and quickness. In this paper, an enhancement shark smell optimization (ESSO) is proposed to build an efficient hybrid identification system depend on the sclera and palm-print images. The SIFT algorithm used to extract features from the biometric images. The optimal key-points from this feature are obtained using ESSO and chaotic map, and finally, generation digital signature using a 256-MD5 algorithm for each user. The Package of the NIST tests proves that the generated keys are random, unpredictable, uncorrelated, and robust against different kinds of attacks.  

Keywords :

SIFT algorithm; Feature Selection; shark smell optimization SSO algorithm; 1d logistic chaotic function.

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Cite this Article as :
Style #
MLA N. A. Majeed alhammadi, K. Hameed Zaboon, A. Abdulhadi Abdullah. "An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing." Journal of Intelligent Systems and Internet of Things, Vol. 10, No. 1, 2023 ,PP. 21-32 (Doi   :  https://doi.org/10.54216/JISIoT.100102)
APA N. A. Majeed alhammadi, K. Hameed Zaboon, A. Abdulhadi Abdullah. (2023). An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 1 ), 21-32 (Doi   :  https://doi.org/10.54216/JISIoT.100102)
Chicago N. A. Majeed alhammadi, K. Hameed Zaboon, A. Abdulhadi Abdullah. "An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing." Journal of Journal of Intelligent Systems and Internet of Things, 10 no. 1 (2023): 21-32 (Doi   :  https://doi.org/10.54216/JISIoT.100102)
Harvard N. A. Majeed alhammadi, K. Hameed Zaboon, A. Abdulhadi Abdullah. (2023). An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 1 ), 21-32 (Doi   :  https://doi.org/10.54216/JISIoT.100102)
Vancouver N. A. Majeed alhammadi, K. Hameed Zaboon, A. Abdulhadi Abdullah. An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 10 ( 1 ): 21-32 (Doi   :  https://doi.org/10.54216/JISIoT.100102)
IEEE N. A. Majeed alhammadi, K. Hameed Zaboon, A. Abdulhadi Abdullah, An optimized Identification System by using Shark Smell algorithm for Biometric Images Crossing, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 10 , No. 1 , (2023) : 21-32 (Doi   :  https://doi.org/10.54216/JISIoT.100102)