Multi-Scale Filter Analysis with a Neutrosophic Similarity Score-Based Enhancement Framework

Abstract Contactless fingerprint acquisition systems encounter significant challenges due to image quality variations, illumination conditions, and evaluation ambiguities that compromise biometric identification accuracy. These challenges constitute Multi-Criteria Decision Making ( $$\text{MCDM}$$ ) problems involving both qualitative and quantitative assessment criteria, which traditional enhancement methods inadequately address due to limited uncertainty quantification capabilities. This paper proposes a novel multi-scale Neutrosophic Similarity Scale ( $$\textrm{NSS}$$ ) enhancement framework for contactless fingerprint recognition. The methodology transforms images into the neutrosophic domain using three membership functions: truth (T), indeterminacy (I), and falsity (F). The framework systematically evaluates multiple filter scales ( $$3 \times 3$$ , $$5 \times 5$$ , $$7 \times 7$$ , $$9 \times 9$$ ) to optimize enhancement performance across varying degrees of image degradation and noise conditions. Comprehensive evaluation integrates objective measures with subjective quality assessments using 5-point fuzzy scales, validated through Intraclass Correlation Coefficient (ICC) and Analysis of Variance (ANOVA). The proposed NSS method with $$3 \times 3$$ filter configuration achieves the highest overall subjective evaluation score of 4.28. Benchmark comparisons with state-of-the-art techniques including U-Net, Feature Pyramid Network (FPN), ResNet, and GAN-based methods demonstrate superior performance in fingerprint feature clarity. Sensitivity analysis and ablation studies validate individual contributions of enhancement criteria $$\mathcal {C}_{\alpha }$$ , $$\mathcal {C}_{\beta }$$ , and $$\mathcal {C}_{\gamma }$$ . Results demonstrate significant improvements in managing vagueness and uncertainty while preserving structural information, establishing the method’s suitability for reliable contactless biometric identification systems.