## 7 Feature Extraction Approaches
### Subspace Learning-Based Approaches
Specifically, to deal with projection issues following the ROI extraction, principal component analysis (PCA), independent component analysis (ICA), and fisher discriminant analysis (FDA) were investigated for dimensionality reduction. Additionally, wavelet transform provided complementary multi-resolution texture characterization. The authors concluded that the configuration involving wavelet + FDA, denoted as WFDA, outperforms other configurations in terms of performance. [PERSON] et al. (2007) [51] proposed a technique known as 2D locality preserving projection (2 DLPP) for palmprint identification. This method focuses on feature extraction and is based on the concept of locality preservation and image matrix projection. To perform the classification, they used a nearest neighbor classifier considering the L2 norm for measuring similarity. [PERSON] and [PERSON] (2008) [52] proposed an approach known as two-dimensional locality preserving projections (I2 DLPP) for palmprint identification. This technique focuses on simplifying computational complexity and reducing feature dimensions by employing two main steps: First, it performs a projection of the training space along the row direction using a two-dimensional principal component analysis (2 DPCA). Thereafter, I2 DLPP is conducted over the resultant compressed columnar direction by employing a nearest neighbor graph. Additionally, the authors propose applying I2 DLPP on Gabor-filtered images (I2 DLPP) to further enrich textural characterization identification. Extensive analysis revealed that the input filtered images significantly improved computational efficiency and identification accuracy. [PERSON] and [PERSON] (2011) [53] proposed an approach called diagonal discriminant locality preserving projections (Dia-DLPP) for the identification of both faces and palmprints. This approach is crafted to capture in both directions--vertical and horizontal--by cueing discriminant information from data. Uniquely, diagonalized images were integrated during the training and testing phases to enhance the discriminative capabilities. The authors additionally proposed a weighted discriminative variant (W2D-DLPP) that explicitly assigns greater significance to more identity-discriminative pixel clusters when computing projection vectors. The discriminative scores are incorporated into the traditional 2D-DLPP technique, resulting in the refined method W2D-DLPP. This integration of discriminative pixel weighting significantly improves the identification performance of both 2D-DLPP and Dia-DLPP, leading to improved accuracy in face and palmprint identification tasks. [PERSON] et al. (2018) [54] introduced a palmprint recognition system that relies on a set of sparse representations (SR). They used two-dimensional principal component analysis (2D-PCA) to build an initial sample dictionary and then used two-dimensional linear discriminant analysis (2D-LDA) to extract discriminative features. [PERSON] et al. [55] devised an ensemble framework leveraging the random subspace method (RSM) for contactless palmprint recognition classification. Two-dimensional principal component analysis (2 DPCA) was applied to obtain multiple dimensional eigenvector random subspaces. Thereafter, two-dimensional linear discriminant analysis (2 DLDA) was conducted within each random 2 DPCA projection to retrieve the most discriminative feature subsets. In addition, Euclidean distances with nearest neighbor classifiers were subsequently implemented on each subspace. Ultimately, a nonlinear decision function was constructed, comprising individual classifiers that vote by majority. [PERSON] et al. (2021) [56] introduced a feature extraction technique for palmprint recognition termed sparse 2D discriminant local preserving projection (SF2 DDLPP) that integrates elasticity into the dimensionality reduction process.