## 7 Feature Extraction Approaches
### Texture-Based Approaches  
Texture-based methods take advantage of the intricate and diverse local features present in palmprint patterns. By exploiting these rich textural features, these methods aim to achieve higher accuracy and reliability in the identification process. Unlike traditional methods that rely solely on global features, texture-based techniques delve into the fine-grained details of the palm surface, capturing an array of minutiae such as ridges, wrinkles, and pores. This meticulous analysis enables the creation of comprehensive and distinctive palmprint templates that facilitate robust and discriminative recognition. As a result, these methods are the cornerstone of biometric authentication systems, where the intricate texture patterns of an individual's palm provide a wealth of information for secure and accurate identity verification. [PERSON] et al. (2014) ([PERSON] et al., 2014) employed a technique involving the division of the complete palmprint image into smaller sub-regions. Within each of these sub-regions, they applied the local binary pattern (LBP) operator to capture the texture characteristics. To enhance recognition efficiency and minimize memory consumption, they introduced a selection process, which retained only the most distinctive areas for the identification task. The sequential forward floating selection (SFFS) algorithm was the basic method employed for this purpose. [PERSON] and [PERSON] (2015) ([PERSON] and [PERSON], 2015) introduced an innovative and straightforward method for palmprint inspection, exploiting the distributed feature representation extracted from the bank of binarized statistical image features (B-BSIF). The BSIF specifically functions as a texture descriptor akin to the LBP, but its distinctiveness lies in its approach to acquiring filters. Unlike the LBP, which manually defines filters, BSIF filters are learned from real images. \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.** & & **\#Imgs.** \\ \hline
2012 & [PERSON] et al. [62] & E-BOCV & PolyU & 384 & 7752 & First session for training, second session for testing & EER = 0.03 \\ \hline
2013 & [PERSON] and [PERSON] [63] & Competitive Coding + TPTRS & PolyU-MS & 500 & 6000 & First 3 Imgs/sub for training, remaining 6 Imgs/sub for testing & 98.00 \\ \hline
2014 & [PERSON] et al. [64] & DR + PCA & PolyU & 100 & 600 & First 3 Imgs/sub for training, remaining for testing & 97.00 \\ \hline
2016 & [PERSON] et al. [65] & DOC & PolyU-MS & 500 & 6000 & First 2 Imgs/sub for training, remaining for testing & 99.27 \\ \cline{3-4} \cline{6-7}  & & IITD & 460 & 2300 & First 2 Imgs/sub for training, remaining for testing & 98.60 \\ \hline
2016 & [PERSON] et al. [66] & DRCC & PolyU & 386 & 7752 & First 2 Imgs/sub for training, remaining for testing & 98.79 \\ \cline{3-4} \cline{6-7}  & & IITD & 460 & 2600 & First 2 Imgs/sub for training, remaining for testing & 98.67 \\ \hline
2020 & Almaghtuf & \multirow{2}{*}{DBM} & PolyU & 386 & 7752 & \multirow{2}{*}{} & 100.00 \\ \cline{3-4} \cline{6-7}  & et al. [67] & & PolyU-MS & 500 & 6000 & First 3 Imgs/sub for training, remaining 3 for testing & 100.00 \\ \cline{3-4} \cline{6-7}  & et al. [67] & & IITD & 230 & 3220 & First 3 Imgs/sub for training, remaining 3 for testing & 95.40 \\ \cline{3-4} \cline{6-7}  & & CASIA & 624 & 5502 & First 2 Imgs/sub for training, remaining 2 for testing & 93.90 \\ \hline
2020 & [PERSON] et al. [68] & MTPSR & PolyU & 386 & 7752 & \multirow{2}{*}{} & 99.58 \\ \cline{3-4} \cline{6-7}  & et al. [68] & GTPS & 100 & 1000 & First 4 Imgs/sub for training, remaining for testing & 96.83 \\ \cline{3-4} \cline{6-7}  & et al. [68] & IITD & 460 & 2601 & & 97.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._[PERSON] and [PERSON] (2016) [71] introduced a palmprint recognition approach.