Genetic Algorithm Based Palm Recognition Method for Biometric Authentication Systems

Čenys, Antanas; Gibavicius, D.; Goranin, Nikolaj; Marozas, Leonardas

doi:10.5755/j01.eee.19.2.3473

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Cenys et al in 2013 suggested a new palm print recognition method that used the GA as a recognition method that does not need any special equipment and can be used in fast palm recognition for identity management and personal authentication. This method is faster by 10 times more than the manual methods, the manual method requires 220 seconds while this method required only 25 seconds or less with the same accuracy which was 96% by using CASIA Dataset [10].…”

Section: Review Of Literaturementioning

confidence: 93%

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Palmprint Identification Using Dolphin Optimization

Al-Taie,

Khaleel

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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Palm print recognition is a rapidly evolving area in the field of biometrics, providing a high level of security for various applications Advances in scanning technology and software have led to faster and more accurate palm print analysis, we have proposed in this paper a system for palm recognition using the Dolphin Optimization Algorithm (DOA) as a computational technique inspired by nature aimed at solving complex improvement problems. We reduced the number of image features using the Histograms of oriented gradients (HOG) algorithm, we named this method as (DOA) and we also proposed a hybrid method by integrating the DOA algorithm with the Support Vector Machine (SVM) model to improve prediction accuracy by combining DSA's ability to search for global optimal solutions with the effective classification capabilities of SVM, this allows The hybrid approach creates a robust and the proposed hybrid method was named (SVM-DOA), and we also proposed a hybrid method by integrating the DOA algorithm with fuzzy c – mean (FCM) and we named the proposed hybrid method (DOA-fuzzy membership), we verified the validity of the proposed method on public database images of palm print. experiment show that the average accuracy rate of the dolphin swarm algorithm (DSO) is (96.8%), while the average accuracy rate of the proposed hybrid algorithm (DSO-SVM) is (97.8%), and the average accuracy of the proposed hybrid algorithm (DSO-fuzzy membership) is (98.1%).

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Section: Review Of Literaturementioning

confidence: 93%

“…Table 3 shows the results of the test phase in the proposed hybrid method (DSO-SVM) when the values of the coefficients are as (HOG -cell size) at (8,16,32), (HOG -block size) at (2), Initial, (population of Dolphin) at (10,30,50) and (Max no. iteration) at (10,30,50).…”

Section: % Precision (9)mentioning

confidence: 99%

Palmprint Identification Using Dolphin Optimization

Al-Taie,

Khaleel

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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“…In paper [ 15 – 17 ] the authors proposed a genetic algorithm based palm recognition method for authentication process. The authors suggested that fingerprint and facial recognition systems are slow and required more expensive technical equipment, while palm recognition method did not require special equipment and could be used in systems where fast detection is required.…”

Section: Related Workmentioning

confidence: 99%

Security Considerations and Recommendations in Computer-Based Testing

Al-Saleem

Ullah

2014

The Scientific World Journal

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Many organizations and institutions around the globe are moving or planning to move their paper-and-pencil based testing to computer-based testing (CBT). However, this conversion will not be the best option for all kinds of exams and it will require significant resources. These resources may include the preparation of item banks, methods for test delivery, procedures for test administration, and last but not least test security. Security aspects may include but are not limited to the identification and authentication of examinee, the risks that are associated with cheating on the exam, and the procedures related to test delivery to the examinee. This paper will mainly investigate the security considerations associated with CBT and will provide some recommendations for the security of these kinds of tests. We will also propose a palm-based biometric authentication system incorporated with basic authentication system (username/password) in order to check the identity and authenticity of the examinee.

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“…The system achieved 95.00% accuracy. A. Cenys et al [4] presented a model that uses genetic algorithm for palm recognition. Several measures of a palm of a person are made on 25 palm print, this method had a 92-96% reliability.…”

Section: Introductionmentioning

confidence: 99%

Multi-biometric System for Security Institutions using Wavelet Decomposition and Neural Network

NajmAbdullah¹,

Hussein²,

Jeiad³

2015

IJCA

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Biometric systems are currently considered one of the leading methods for security and access control systems. The use of multi-biometric in verification and identification provides more reliability and accuracy for such systems. In this paper three biometric traits have been used face, iris and fingerprint for identification purpose. After preprocessing feature extraction for each trait, wavelet decomposition was used. Back-propagation neural network was employed for the training of the system. The results showed a highly accurate recognition rate after 298 epoch of training. A measurement of MSE and PSNR. With false acceptance rate (FAR) of 0% and false rejection rate (FRR) of 3% were calculated for system performance evaluation.

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Genetic Algorithm Based Palm Recognition Method for Biometric Authentication Systems

Cited by 4 publications

References 16 publications

Palmprint Identification Using Dolphin Optimization

Palmprint Identification Using Dolphin Optimization

Security Considerations and Recommendations in Computer-Based Testing

Multi-biometric System for Security Institutions using Wavelet Decomposition and Neural Network

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