Efficient Contactless Palmprint Recognition System Based on Deep Rule-Based Classification

Abstract

In recent years, as technology has advanced and more and more activities have become digitized, cybersecurity has become a top priority for governments around the world. Cybersecurity is essential for protecting computer systems, networks and data from cyberattacks that can have a negative impact on individuals, businesses and governments. Indeed, biometrics is a key means of cybersecurity that can help prevent unauthorized access, identity theft and unauthorized changes to data. This paper presents an innovative contactless palmprint recognition system, integrating two types of features to enhance accuracy and efficiency. Our approach employs two distinct feature sets: handcrafted features, based on the Pyramid Histogram of Oriented Gradients (PHOG) and Local Phase Quantization (LPQ) techniques and deep features extracted through deep learning-based image analysis methods such as DCTNet, DSTNet, PCANet and ICANet. Furthermore, we used a sophisticated Deep Rule-based (DRB) classifier for classification tasks. Experimental results obtained using a typical database demonstrated excellent identification rates, surpassing significantly those reported in similar studies.

Keywords

• cybersecurity
• biometrics
• drb
• lpq
• phog
• dctnet
• dstnet
• pcanet
• icanet
• deep rule-based classifier