• Anindya Mondal

Fingerprint recognition is a critical component of biometric authentication systems, finding applications in various domains such as forensic investigations, secure access control, and identity verification. Traditional fingerprint-matching methods often face challenges in handling diverse image conditions, such as variations in image quality, noise, and distortions. This paper explores the integration of machine learning techniques to enhance the robustness of fingerprint matching in image processing. The proposed approach leverages state-of-the-art machine learning algorithms, including deep neural networks, support vector machines, and ensemble methods, to address the inherent limitations of conventional fingerprint-matching techniques. By employing these advanced learning models, the system can adapt to complex fingerprint patterns and variations, leading to improved accuracy and reliability in matching fingerprints across challenging conditions. The study also investigates the utilization of large-scale fingerprint datasets to train and fine-tune the machine-learning models, allowing them to learn intricate patterns and subtle variations in fingerprint images. Additionally, feature extraction techniques are explored to represent fingerprint minutiae and ridge patterns effectively, enabling the models to capture and differentiate unique fingerprint characteristics. The experimental results demonstrate the efficacy of the proposed machine learning approaches in achieving robust and accurate fingerprint matching across various scenarios, including low-quality images and partial fingerprints. The comparative analysis with traditional methods highlights the superior performance of the machine learning-based approach in terms of both matching accuracy and computational efficiency. In conclusion, this research contributes to the advancement of fingerprint recognition systems by integrating machine learning techniques and enhancing the robustness and adaptability of the system to challenging image conditions. The findings pave the way for the development of more reliable biometric authentication systems with broader applications in security and identification domains

Machine Learning, Image Processing, Fingerprint matching, Deep Neural Networks, Support Vector Machines

"Machine Learning Approaches for Robust Fingerprint Matching in Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.324-329, July-2018, Available :http://www.jetir.org/papers/JETIR1807A49.pdf

"Machine Learning Approaches for Robust Fingerprint Matching in Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp324-329, July-2018, Available at : http://www.jetir.org/papers/JETIR1807A49.pdf