Published Paper Details:

Automated fingerprint recognition has emerged as a widely utilized tool for personal identification and various biometric applications, owing to its dependable and distinctive characteristics. Fingerprint classification stands out as a crucial task in the context of large-scale fingerprint recognition systems. This task is inherently challenging due to factors such as limited interclass variability, substantial intraclass variability, the presence of noise, and the inherently ambiguous nature of fingerprints. Effectively addressing the complexities of fingerprint image classification requires sophisticated pattern recognition solutions. A precise classification algorithm holds the potential to significantly reduce the number of comparisons required during fingerprint retrieval, thereby expediting the identification process. Over the last few decades, a substantial body of research has been dedicated to devising techniques for distinguishing different fingerprint classes. This paper presents a comprehensive review of existing approaches employed in addressing fingerprint classification challenges. The discussion encompasses key issues, design considerations, and the performance evaluation of various techniques within the realm of fingerprint classification systems.