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.
