A Lightweight Fingerprint Recognition Pipeline Based on Hierarchical Energy-Feature Decomposition

Fingerprint recognition remains challenging when ridge structures are degraded by noise, weak contrast, translation, rotation, and local deformation during acquisition. Although deep-learning approaches have improved biometric recognition, they often require large labeled datasets and carefully specified training protocols, which can limit their use in small-data or resource-constrained scenarios. This paper presents a lightweight fingerprint recognition pipeline based on hierarchical energy-feature decomposition. The pipeline integrates Template Integrated Mean (TIM) preprocessing, region-of-interest localization, coefficient-feature extraction, energy-feature extraction, and two-stage template matching. Coefficient features are used for coarse candidate screening, whereas energy features are used for fine matching within the reduced candidate set. On the evaluated fingerprint dataset, the proposed method achieves a closed-set identification accuracy of 97.86% under the reported gallery/probe protocol. Additional aggregate-level statistical checks and baseline configuration details are provided to clarify the evaluation scope and comparison protocol.