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@article{188457,
        author = {Jainy Jacob M},
        title = {Enhancing Fingerprint Recognition Performance using KNN classifier and modified XG Boost Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2906-2912},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188457},
        abstract = {With the increasing use of biometric identification systems, there is a growing concern about the possibility of fake fingerprints being used to bypass security measures. Fingerprint recognition using KNN (K-Nearest Neighbors) algorithm incorporating modified optimization algorithm is an effective approach for biometric identification. KNN is a simple but powerful algorithm that can be trained on a dataset of fingerprint images and their corresponding labels to classify new images based on their nearest neighbors in the training set. The algorithm can be optimized by incorporating modified XGBoost, a gradient boosting algorithm, to improve its performance. The proposed approach of improving fingerprint recognition performance using a modified XGBoost algorithm and K-Nearest Neighbor classifier is a promising algorithm can effectively exploit the complementary strengths of both approaches and achieve superior performance in terms of accuracy and efficiency},
        keywords = {Gradient boosting, K-Nearest Neighbors, optimization algorithm, XGBoost, AdaBoost.},
        month = {December},
        }