## 7 Feature Extraction Approaches
### Deep Learning-Based Approaches  
In this category, the methods frequently employed convolutional neural networks (CNNs). These networks comprise convolutional layers, pooling layers, and fully connected layers, allowing for simultaneous feature extraction and classification [79; 80]. [PERSON] et al. (2018) [81] proposed a system consisting of three main modules. (i) The Region of Interest Extraction Module (REM) is responsible for extracting palmprint ROIs using a bounding box approach. First, the input images are subjected to a preprocessing step. Then, a transfer learning technique using CNNs is applied to identify the optimal placement of bounding boxes on the palmprint images. This module extracts regions of interest (ROIs) from the palmprint, which serve as input for the subsequent feature extraction module. (ii) In the Feature extraction module (FEM), features are represented using a pre-trained CNN architecture. It applies the learned representations to extract discriminative features from the palmprint ROIs. (iii) The matching module (MM) takes the feature vector generated in the previous step as input and uses a machine learning classifier to perform the recognition task. [PERSON] et al. (2019) [18] developed an end-to-end deep learning approach named the end-to-end palmprint recognition network (EE-PRnet). This network comprises two fundamental components: ROI localization and alignment network (ROI-LAnet) and feature extraction and recognition network (FERnet). ROI-LAnet is tasked with transforming all input palmprint images into a consistent coordinate system and delineating the ROI containing distinguishing structural information. ROI-LAnet comprises two segments: the first is a pre-trained VGG-16 network with its top layers removed, and the second is a fully connected regression network. FERnet is tasked with extracting and recognizing palmprint features. This network is a self-contained CNN based on a modified VGG-16 architecture. [PERSON] et al. (2019) [30] proposed the use of two separate comprehensive CNN systems, named PalmNet and GenderNet. These networks were thoroughly trained to excel in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Used Datasets**} & \multirow{2}{*}{**Evaluation Protocol**} & \multirow{2}{*}{**Acc. (\%)**} \\ \cline{3-4} \cline{6-7}  & & & **Name** & **\#IDs.** & **\#Imsg.** & & First 10 Imgs/sub for training, & \\
2018 & [PERSON] et al. [73] & WACS-LBP + & PolyU & 386 & 7752 & remaining 10 Imgs/sub for testing & 99.14 \\  & [73] & WSRC & CASIA & 624 & 5502 &
\begin{tabular}{c} 7 Imgs/sub for training, remaining \\ for testing (50 Rand iterations) \\ \end{tabular} & 98.06 \\
2019 & [PERSON] et al. [74] & PCMBP + & PolyU & 500 & 6000 & \
\begin{tabular}{c} First session for training, second \\ session for testing \\ \end{tabular} & 99.34 \\  & et al. [74] & PhOG & & PolyU & 352 & 7040 &
\begin{tabular}{c} First session for training, second \\ session for testing \\ \end{tabular} & \(\approx\)97.00 \\
2019 & [PERSON] et al. [75] & \begin{tabular}{c} LBP + Spiral \\ features + \\ mRMR \\ \end{tabular} & IITD & 470 & 2350 & \
\begin{tabular}{c} 4 Imgs/sub for training, remaining \\ for testing \\ \end{tabular} & \(\approx\)98.00 \\  & & PolyU-MS & 500 & 6000 &
\begin{tabular}{c} First session for training, second \\ session for testing \\ \end{tabular} & \(\approx\)99.00 \\ \hline
2020 & [PERSON] & \multirow{2}{*}{2 DCT} & IITD & 200 & 1200 & \multirow{2}{*}{50\% for training (first Imgs), 50\% for testing} & 97.85 \\  & and & CASTA & 312 & 2496 & \
\begin{tabular}{c} 50\% for training (first Imgs), \\ 50\% for testing \\ \end{tabular} & 98.66 \\  & [76] & & PolyU & 386 & 7752 &
\begin{tabular}{c} 99.80 \\ \end{tabular} \\ \hline
2020 & [PERSON] et al. [77] & HMS-CLBP + & PolyU & 386 & 7752 &
\begin{tabular}{c} 5-fold cross-validation \\ (50 iterations) \\ \end{tabular} & 99.68 \\
\begin{tabular}{c} \hline \hline \end{tabular}
\end{table}