## 7 Feature Extraction Approaches
### Deep Learning-Based Approaches
Extensive experiments demonstrated significant gains over conventional approaches, constituting a vital step towards palmprint identification systems. [PERSON] et al. (2023) [86] devised a hybrid palmprint recognition framework fusing deep learning and classical machine learning methodologies. Their processing pipeline starts with multiple preprocessing interventions encompassing boundary delineation, binarization, finger exclusion, edge contour extraction, noise filtering, and image thinning to maximize distinctive friction ridge information. The second step focuses on extracting the ROI for palmprint images. Thereafter, a CNN feature extractor learns hierarchical representations encoding both textural and topological traits. Finally, the CNN embeddings are classified using CNN classifier gathering with support vector machine (SVM) classifiers for gallery identity prototypes for palmprint identification. Table 7 provides a summary of the studies discussed in this subsection, presenting the feature extraction methods used, the datasets employed, the experimental protocols, and the key findings. \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{5-6}  & & & **Name** & **\#IDs.** & **\#Imgs.** & & 99.40 \\ \hline \multirow{3}{*}{2018} & Izadpanahkakhk & \multirow{3}{*}{Fast CNN} & PolyU & 386 & 7752 & First 4 Imgs/sub for training, remaining for testing & 98.30 \\ \cline{3-6}  & & & & IITD & 460 & 2600 & 94.70 \\ \hline \multirow{3}{*}{2019} & \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{EE-PRnet} & CASIA & 618 & 5502 & First 4 Imgs/sub for training, remaining for testing & 97.65 \\ \cline{3-6}  & & & IITD & 460 & 2601 & 99.61 \\ \cline{3-6}  & & & IITD & 460 & 2601 & 99.77 \\ \cline{3-6}  & \multirow{3}{*}{[18]} & & & & & 99.77 \\ \cline{3-6}  & & & & & & 95.34 \\ \cline{3-6}  & & & & & & (Rand) & 41.92 \\ \hline \multirow{3}{*}{2019} & \multirow{3}{*}{[PERSON] et al. [30]} & \multirow{3}{*}{PalmNet + GenderNet} & PolyU2D/3D & 177 & 1770 & 98.98 \\ \cline{3-6}  & & & IITD & 460 & 2601 & 99.01 \\ \cline{3-6}  & & & & & & 99.50 \\ \cline{3-6}  & & & & & & 99.18 \\ \cline{3-6}  & & & & & & 100.00 \\ \cline{3-6}  & & & & & & 100.00 \\ \cline{3-6}  & & & & & & 95.16 \\ \hline \multirow{3}{*}{2019} & \multirow{3}{*}{[PERSON] et al. [82]} & PalmNet-GaborPCA & CASIA & 624 & 5455 & 99.77 \\ \cline{3-6}  & & & & IITD & 467 & 2669 & 99.37 \\ \cline{3-6}  & & & & & & 97.16 \\ \cline{3-6}  & & & & & & 99.83 \\ \cline{3-6}  & & & & & & 99.41 \\ \cline{3-6}  & & & & & & 98.70 \\ \cline{3-6}  & & & & & & 99.95 \\ \hline \multirow{3}{*}{2020} & \multirow{3}{*}{[PERSON] and [PERSON] [83]} & \multirow{3}{*}{DDR} & CASIA & 624 & 5500 & 99.77 \\ \cline{3-6}  & & & & & & 99.37 \\ \cline{3-6}  & & & & & & 97.16 \\ \cline{3-6}  & & & & & 99.83 \\ \cline{3-6}  & & & & & & 99.41 \\ \cline{3-6}  & & & & & & 98.70 \\ \cline{3-6}  & & & & & & 5 iterations) & 99.95 \\ \hline \multirow{3}{*}{2020} & \multirow{3}{*}{[PERSON] and [PERSON] [24]} & \multirow{3}{*}{RFN} & IITD & 230 & 1150 & 5 left Imgs for training, 5 right Imgs for testing & 99.20 \\ \cline{3-6}  & & & & & & 98.94 \\ \cline{3-6}  & & & & & & 98.73 \\ \cline{3-6}  & & & & & & 98.73 \\ \cline{3-6}  & & & & & & 97.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparative overview of deep learning-based approaches (#ID: number of identities, #Imgs: number of images, Acc: valid accuracy).