Precision in Biometric Authentication: CNN-driven Fingerprint Classification

Fingerprint identification is often regarded as a fundamental component of biometric security systems, as it provides an exceptional means of recognizing individuals based on the distinctiveness and enduring nature of their fingerprints. This paper presents a novel Convolutional Neural Network (CNN) model designed explicitly for categorizing fingerprints, which plays a critical role in biometric identification systems essential for ensuring security and personal authentication. This study extensively investigates the capability of CNNs in fingerprint classification, utilizing the 'Sokoto Coventry Fingerprint Dataset (SOCOFing),' which consists of 55,270 annotated fingerprint pictures. The comprehensive classification of the dataset, based on the identification of fingers and hands, establishes a solid basis for comprehending a wide range of fingerprint patterns and structures. The dataset has been carefully partitioned into training, validation, and testing segments, facilitating thorough model building. The CNN model demonstrates its proficiency in generalization and accurate fingerprint classification through extensive training on a dataset consisting of 39,416 photos and thorough fine-tuning utilizing an additional 9,854 validation images. Performance evaluation in machine learning involves the utilization of accuracy and loss diagrams to visualize the model's learning progress during training. Additionally, a confusion matrix is employed during the testing phase to assess the model's performance. The evaluation results indicate a remarkable accuracy rate of 99.88%. The findings of this study highlight the high level of accuracy exhibited by the CNN model in classifying fingerprints according to finger and hand attributes. This study aims to provide a comprehensive understanding of the significant role that fingerprint identification plays in enhancing the effectiveness of biometric security systems. As demonstrated, the CNN model's accuracy and efficacy validate its potential to enhance biometric authentication systems, facilitating the development of more secure and dependable individual authentication procedures.