Palmprint Recognition Using Pre-Trained Convolutional Neural Networks

Abstract

Palmprint recognition (PR) has garnered significant interest due to its distinctive characteristics and potential applications as a robust and secure biometric authentication (BA) method across various domains. This paper introduces a novel approach to enhance PR using pre-trained Convolutional Neural Network (CNN) models. The proposed method process harnesses the capabilities of Deep Learning (DL) and Transfer Learning (TL) to improve palmprint recognition by leveraging state-of-the-art CNN architectures for feature extraction and classification. This proposed method begins by exploring the potential of pre-trained CNN architectures, including ShuffleNet, EfficientNet, and MobileNet, as feature extractors for palmprints. In the next step, extracted feature vectors are compared using the Cosine Similarity (CS) method. The proposed method is thoroughly evaluated through comprehensive experiments. Results of evaluations demonstrate that pre-trained CNN models excel at recognizing palmprints for biometric authentication, establishing their proficiency in this domain. Consequently, this paper illuminates the inherent capabilities of pre-trained CNN models as a potent tool for advancing PR, introducing an innovative facet to BA methodologies.