## 7 Feature Extraction Approaches
### Local Direction Encoding-Based Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Used Datasets**} & \multirow{2}{*}{**Evaluation Protocol**} & \multirow{2}{*}{**Acc. (\%)**} \\ \cline{5-6} \cline{8-8}  & & & **Name** & **\#IDs.** & **\#Imgs.** & \\ \hline \multirow{6}{*}{2022} & \multirow{6}{*}{[PERSON] et al. [57]} & & IITD & 460 & 2601 & \multirow{6}{*}{4 Imgs/sub for training, remaining for testing (Rand, 10 iterations)} \\ \cline{3-4} \cline{6-8}  & & & GPDS & 100 & 1000 & & 98.93 \\ \cline{1-1} \cline{3-4} \cline{6-8}  & & DC\_MDPR & CASIA & 624 & 5500 & \multirow{6}{*}{4 Imgs/sub for training, remaining for testing (Rand, 10 iterations)} \\ \cline{3-4} \cline{6-8}  & & & TJI & 600 & 12,000 & & 99.44 \\ \cline{3-4} \cline{6-8}  & & & PV\_790 & 418 & 4180 & & 99.76 \\ \hline \multirow{2}{*}{2023} & \multirow{2}{*}{[PERSON] et al. [58]} & & \multirow{2}{*}{LR-2 DDGE} & \multirow{2}{*}{PolyU} & \multirow{2}{*}{100} & \multirow{2}{*}{600} & 50\% for training (Rand, 10 iterations), 50\% for testing & \\ \cline{3-4} \cline{6-8}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._[PERSON] and [PERSON] (2013) [63] suggested employing a weighted fusion scheme integrating two-phase test sample sparse representation (TPTSR) with competitive coding methods for palmprint identification. First, a competitive coding algorithm is employed to obtain the directional matching score of two images. Thereafter, TPTSR is computed globally to match the score of the two images. Finally, the two scores are added together to categorize the test sample. [PERSON] et al. (2014) [64] presented directed representations (DRs) for palmprint identification. First, a representation is proposed for an appearance-based technique based on multiple anisotropic filters. Subsequently, the feature extraction and the dimension reduction are guaranteed using the PCA technique. Finally, a compressed sensing classification step is implemented to distinguish between palms of different hands. [PERSON] et al. (2016) [65] presented a robust palmprint recognition method, the double-orientation code (DOC) approach. This technique offers a reliable way to represent palmprint orientation features, as shown through an investigation of palmprint orientation-based coding theory. They also introduced a novel nonlinear angular matching score metric for efficient similarity assessment between DOC-encoded palmprints, boosting the overall effectiveness of the technique in palmprint identification. [PERSON] et al. (2016) [66] introduced a palmprint identification technique called discriminative and robust competitive code (DRCC), which emphasizes discriminative and robust techniques based on dominant orientation. Their approach combines dominant orientation and lateral codes to capture important orientation features in palmprints. Strategic weighting during orientation extraction improves accuracy while using the same Gabor filters as the conventional method. This innovation holds promise for accurate and efficient palmprint orientation extraction. [PERSON] et al. (2020) [67] proposed a palmprint coding technique known as difference of block means (DBM). To derive the palmprint code, they followed the recommended approach: First, they computed the difference between overlapping block means of identical size within the interest area of the palmprint to extract palm-related information in both the vertical and horizontal directions. Then, vertical and horizontal codes were generated by applying thresholding to the DBM features. Finally, the Hamming distance, which is the average of the vertical and horizontal distances, was used for the matching step. [PERSON] et al. (2020) [68] developed a multi-feature palmprint recognition framework predicated on modeling orientation field patterns termed histograms of line mixed distances (HODlm) alongside histograms of response distances (HODr).