## 7 Feature Extraction Approaches
### Subspace Learning-Based Approaches  
Subspace learning-based methods work by extracting and assimilating key features from a palmprint image through the acquisition of a latent subspace guided by a variety of constraints. These methods go beyond traditional feature extraction techniques by dynamically learning and encapsulating the most salient aspects of palmprint patterns into a lower dimensional subspace. By incorporating constraints that include structural, statistical, and contextual information, these techniques meticulously fine-tune their subspace representations to capture the subtle intricacies of individual palmprint variations. This process effectively distills the complexity of palmprint data into a more compact and discriminative form, laying the groundwork for increased recognition accuracy. Subspace learning-based methods emerge as powerful tools to decipher the underlying palmprint data structure, allowing for the creation of highly informative templates that are adept at detecting minute differences while also accounting for broader pattern trends. [PERSON] et al. (2003) [50] proposed the Fisherpalm, a palmprint recognition system founded on fisher's linear discriminant (FLD) analysis. Within this approach, every palmprint is treated as a point within a high-dimensional image space. Palmprints are then mapped from this high-dimensional space to a much lower-dimensional feature space. This trans \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 \multirow{4}{*}{2016} & \multirow{4}{*}{[PERSON] et al. [46]} & & PolyU & 386 & 7752 & First 3 Imgs from 1 st session for training, second session for testing & 100.00 \\ \cline{3-4} \cline{6-7}  & & & PolyU-MS & 500 & 6000 & First 3 Imgs from 1 st session for training, second session for testing & 100.00 \\ \cline{3-4} \cline{6-7}  & & & Cross-Sensor & 200 & 12,000 & & for testing & 98.45 \\ \cline{3-4} \cline{6-7}  & & & IITD & 460 & 2601 & First Img/sub for training, remaining (4–6) for testing & 92.00 \\ \hline \multirow{4}{*}{2017} & \multirow{4}{*}{[PERSON] et al. [47]} & & PolyU & 386 & 7752 & All Imgs from 1 st session for training, 5 Imgs (Rand) from second session for testing & 96.99 \\ \cline{3-4} \cline{6-7}  & & Principal & & & & & \\ \cline{3-4} \cline{6-7}  & & lines + texture pattern & CASIA & 312 & 5502 & 3 Imgs/sub for testing (only right hands) & 98.00 \\ \cline{3-4} \cline{6-7}  & & & IITD & 460 & 2300 & 3 Imgs/sub for training, 2 Imgs/sub for testing & 97.98 \\ \hline \multirow{4}{*}{2018} & \multirow{4}{*}{[PERSON] et al. [48]} & & PolyU-MS & 500 & 6000 & First 3 Imgs from 1 st session for training, 6 Imgs from second session for testing & 99.22 \\ \cline{3-4} \cline{6-7}  & & & CASIA & 614 & 5502 & 6 Imgs/sub for training, remaining for testing (Rand) & 97.75 \\ \cline{3-4} \cline{6-7}  & & & TJI & 600 & 12,000 & First session for training, second session for testing & 98.85 \\ \hline \multirow{4}{*}{2019} & \multirow{4}{*}{[PERSON] et al. [49]} & & PolyU-MS & 500 & 6000 & First 3 Imgs from 1 st session for training, 6 Imgs from second session for testing & 99.83 \\ \cline{3-4} \cline{6-7}  & & & PolyU & 386 & 7752 & 98.85 \\ \cline{3-4} \cline{6-7}  & & & CASIA & 614 & 5502 & 5-fold cross-validation & 97.02 \\ \cline{3-4} \cline{6-7}  & & & IITD & 460 & 2601 & 94.79 \\ \cline{3-4} \cline{6-7}  & & & COEP & 168 & 1344 & 97.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._formation enhances the system's ability to effectively discriminate between the palmprints of different individuals. [PERSON] et al. (2005) [2] introduced an automated system for palmprint recognition utilizing a peg-free scanner.