Fingerprint Classification Using Kmcg Algorithm under Varying Window and Codebook Sizes

Abstract

Fingerprint classification is a key task in biometric recognition because it supports faster identification, verification, and retrieval in automated fingerprint systems. However, accurate classification remains difficult when fingerprint images contain similar ridge patterns, noise, partial impressions, or variations caused by acquisition conditions. Traditional machine learning methods are efficient and interpretable, but their performance depends on the quality of handcrafted features. Deep learning models often achieve higher accuracy, but they require greater computational resources and provide limited transparency. This study evaluated fingerprint classification using Kekre’s Median Codebook Generation (KMCG) under varying window and codebook sizes and compared its performance with Principal Component Analysis (PCA) and deep learning approaches. The main objective was to determine the effectiveness of KMCG-based feature extraction and compare its performance with PCA-based machine learning and deep learning models. Fingerprint images from the NIST Special Database 302 were used. KMCG extracted texture descriptors under varying window and codebook sizes, while PCA served as a dimensionality-reduction baseline. Support Vector Machine, K-Nearest Neighbours, Random Forest, and XGBoost classifiers were trained using the extracted features. Three convolutional neural network architectures were also implemented to represent different deep learning capabilities: MobileNetV2 was selected as a lightweight and computationally efficient model, InceptionV3 was used to evaluate multiscale feature extraction, and DenseNet201 was included to assess whether dense feature reuse and greater network depth improved fingerprint classification. Performance was evaluated using accuracy, precision, recall, F1-score, average score, confusion matrices, and execution time. MobileNetV2 achieved the best overall performance, with an accuracy of 91.7% and an average score of 94.9% across the evaluation metrics. DenseNet201 achieved 90.0% accuracy, while InceptionV3 recorded 83.4%. Among the traditional machine learning approaches, PCA-XGBoost obtained the highest accuracy of 73.8%, followed by KMCG-SVM at 70.2%. These findings demonstrate that deep learning models provide greater classification accuracy, although KMCG-based approaches remain useful where computational efficiency, compact feature representation, and interpretability are important. The study therefore provides a comparative framework for selecting fingerprint-classification approaches according to performance requirements and available computing resources.