Local Extrema Gabor Patterns: A Hybrid Approach for Enhanced Palmprint Recognition

Introduction: Palmprint recognition is an emerging field within biometric-based personal identification, offering greater security than traditional ID cards or password-based systems. While biometric methods using traits like fingerprints and facial features are well-studied, palmprint identification remains relatively underexplored despite its strong potential. This paper introduces a novel feature descriptor, Local Extrema Gabor Patterns (LEGP), for palmprint recognition. Methods: The proposed method combines the Gabor Transform (GT) with local extrema, effectively capturing the directional extrema relationships between a reference pixel and its surrounding neighbors. Results: The results demonstrate that the LEGP outperforms state-of-the-art techniques like LBP, LDP, LTP, and their Gabor-enhanced variants (GLBP, GLDP, GLTP), achieving a significantly higher recognition rate (RR). Discussion: Existing approaches primarily use Gabor Transform (GT) features from the transform domain and Local Binary Patterns (LBP) from the spatial domain. In contrast, our proposed method combines both domains by introducing LEGP, a new spatial feature. While LBP captures pixel relationships, LEGP extracts directional extrema from Gabor transform responses. Conclusion: The proposed Local Extrema Gabor Patterns (LEGP) descriptor enhances palmprint recognition by integrating the Gabor Transform with local extrema analysis to capture directional features. Evaluated on the HKPU Palmprint database, LEGP outperforms existing pattern-based methods in terms of recognition rate, achieving 99.53% accuracy with a processing time of 76.2 ms.