###### 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 yields 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. biometrics; palmprint recognition; feature extraction; deep learning; datasets +
Footnote †: journal:  
preprocessing techniques, encompassing noise reduction, normalization, and enhancement, to ensure consistent submission despite any restrictions on the availability of materials and/or refined input data. Following preprocessing, relevant features are extracted, like minutiae points specific to an individual's palm, ridges, and lines. These features are crucial for accurate identification and are obtained through advanced image processing methods [2]. Palmprint recognition systems possess notable advantages--notably their non-intrusive nature, stability of features over time, and the abundance of unique identifying characteristics on the palm [3]. However, challenges persist, including variations in illumination, pose, and image quality, necessitating meticulous attention for precise and dependable recognition outcomes. Sustained research efforts, especially within image processing, machine learning, and deep learning, have significantly enhanced palmprint recognition systems. These advancements have solidified the integration of palmprint recognition as an indispensable component within contemporary biometric security applications [4]. Despite promising advancements in palmprint recognition, numerous unresolved issues and open challenges persist in the field. These challenges encompass diverse factors such as changes in pose, occlusion, blurring, image resolution, and the synthesis of palmprints [5; 6]. Successfully addressing these challenges requires substantial efforts aimed at enhancing palmprint acquisition, normalization techniques, and recognition algorithms. Such efforts are crucial for deploying palmprint recognition within forensic analysis, surveillance systems, mobile phone security, and various commercial applications. To confront and deal with these challenges, this paper provides a meticulous analysis that highlights notable advances that have significantly influenced the development of palmprint biometric recognition. This discussion covers historical evolutions up to the present, with focused attention on anticipating future directions in the field. In this comprehensive review, our main contributions can be succinctly outlined as follows:  
* We deliver a timely, thorough, and concise review of the extensive literature on image-based palmprint recognition. This includes an analysis of both contact and contactless palmprint databases, along with the employed evaluation methods. Our evaluation covers 20 databases and more than 60 publications from late 2002 to 2023.