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This research aims to enhance the quality of touchless fingerprint images through a novel image processing framework.

June 26, 2026Open Access

Image enhancement on fuzzy based neutrosophic sets for touchless fingerprint via fractional sobel operator

JRJenisha RachelVellore Institute of Technology University EDEzhilmaran DevarasanVellore Institute of Technology University

Key Points

This research aims to enhance the quality of touchless fingerprint images through a novel image processing framework.
Proposed a Neutrosophic Set Fractional Sobel Enhancer (NS−FSE) for fingerprint image processing.
Conducted experiments with 2,976 fingerprint images from the PolyU database.
Compared the proposed method with seven existing enhancement techniques based on various quality metrics.
Achieved an Equal Error Rate of 7.3% compared to 18.2% for unenhanced images.
Significant improvements in ridge clarity, minutiae quality, and overall clarity score (p < 0.001).
Demonstrated effective indeterminacy reduction and edge enhancement in fingerprint images.

Abstract

Touchless fingerprint recognition provides hygienic and user-friendly biometric acquisition; however, image quality is often degraded by non-uniform illumination, motion blur, low ridge-to-valley contrast, and background interference. These factors disrupt ridge continuity and minutiae extraction, leading to poor recognition accuracy. To address these challenges, this paper proposes a novel Neutrosophic Set Fractional Sobel Enhancer (NS−FSE) for touchless fingerprint images. The proposed framework transforms fingerprint images into the neutrosophic domain using three structurally independent components: truth, indeterminacy, and falsity, derived respectively from pixel intensity, local entropy and variance, and gradient magnitude. These components model ridge information, ambiguity, and background uncertainty independently without mutual constraints. An adaptive indeterminacy reduction mechanism suppresses ambiguous regions while preserving ridge structures. Subsequently, a fractional-order Sobel operator enhances ridge edges while minimizing noise amplification. The enhanced image is reconstructed using a nonlinear defuzzification process. Experiments were conducted on 2,976 touchless fingerprint images from two independent acquisition sessions of the PolyU database. Recognition performance was evaluated using a unified cross-session dataset containing 160 genuine and 25,440 impostor pairs with the SourceAFIS matcher. The proposed method was compared with seven enhancement techniques using conventional image quality and fingerprint-specific structural metrics. Results show significant improvements in ridge clarity, minutiae quality, ridge continuity, and overall clarity score. The proposed approach achieved an Equal Error Rate of 7.3% compared to 18.2% for unenhanced images. Ablation studies and paired t-tests (p < 0.001) further confirm the effectiveness and statistical significance of the proposed framework.

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