Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Abstract

This paper presents a comprehensive survey examining the prevailing feature extraction methodologies employed within biometric palmprint recognition models. It encompasses a critical analysis of extant datasets and a comparative study of algorithmic approaches. Specifically, this review delves into palmprint recognition systems, focusing on different feature extraction methodologies. As the dataset wields a profound impact within palmprint recognition, our study meticulously describes 20 extensively employed and recognized palmprint datasets. Furthermore, we classify these datasets into two distinct classes: contact-based datasets and contactless-based datasets. Additionally, we propose a novel taxonomy to categorize palmprint recognition feature extraction approaches into line-based approaches, texture descriptor-based approaches, subspace learning-based methods, local direction encoding-based approaches, and deep learning-based architecture approaches. Within each class, most foundational publications are reviewed, highlighting their core contributions, the datasets utilized, efficiency assessment metrics, and the best outcomes achieved. Finally, open challenges and emerging trends that deserve further attention are elucidated to push progress in future research.

Boosting the Performance of Deep Ear Recognition Systems Using Generative Adversarial Networks and Mean Class Activation Maps

“…Convolutional neural networks (CNNs) have been clearly identified as a reliable data learning technique for solving complex computer vision problems [7][8][9]. These developments have facilitated the creation of an end-to-end model that includes simultaneous feature extraction and classification steps [10].…”

Enhanced histogram of oriented lines for palmprint recognition

“…The use of palmprints in biometric authentication has dramatically increased and has been used extensively in life. In this field, the palmprint is considered a new modality, a unique entity that is stable over time and has a rich information structure [5,6] . For palmprint recognition, many effective methods have been proposed [7][8][9][10][11] .…”