IMPROVING BIOMETRIC AUTHENTICATION: ADVANCED TECHNIQUES FOR FINGERPRINT MINUTIAE EXTRACTION
MINAR International Journal of Applied Sciences and Technology · 2025
Recently, various methods of biometric identification have emerged, the most prominent of which is fingerprint.However, the strategy of extracting accurate details from them remains a hot topic and a problem that needs to be solved in order to achieve the highest levels of security when applied in various applications.Therefore, this article is used to developing a model based on hybrid deep learning with high flexibility and security by using it with improved Gabor filtering, which provides the characteristics of accuracy, speed, and high adaptability in order to be able to detect accurate details.The proposed model provides a solution to many challenges, the most important of which are the accuracy of low-quality images and the presence of noise.The performance of it is tested by calculating precision, recall, and F1-score and then comparing it with a set of previous studies.The results showed that the proposed model achieved an accuracy in the ability to detect and extract fine details by 98.6%, which is an improvement of 20% over traditional techniques.There is also an increase in processing speed by 30%, with a decrease in false positives and lost fine details.The proposed approach outperforms conventional techniques in accuracy and scalability, paving the way for next-generation biometric systems.