## 7 Feature Extraction Approaches
### Texture-Based Approaches
Initially, the region of interest (ROI) is obtained from palmprint images. Subsequently, to mitigate computational costs and noise, first-level decomposition is performed on the ROI by employing the Haar wavelet. Combining this image with its block-wise histograms, which statistically summarize local variations, yields a comprehensive descriptor called the block-wise Gaussian derivative phase pattern histogram (BGDPPH). Having extracted robust features, kernel discriminant analysis (KDA) is applied to refine their discriminative power. Finally, the Euclidean distance is used for classification. [PERSON] et al. (2018) [72] proposed a local palmprint feature descriptor based on the Gabor wavelet, a local phase quantization (LPQ) descriptor and a spatial pyramid histogram (SPH) descriptor for palmprint image extraction. First, the Gabor wavelet and the LPQ are used to recover invariant blur for multiscale and multi-orientation features. The SPH is then employed for vertical decomposition to concatenate a group of local features into a large histogram called the Gabor LPQ special pyramid histogram (GLSPH) feature for each image. The GLSPH features are then projected into a whitened linear discriminant analysis (WLDA) subspace to reduce their dimensions and make them further discriminative, resulting in the DGLSPH feature. Ultimately, to deal with classification, the K-nearest neighbor (K-NN) classifier is employed in this context. [PERSON] et al. (2018) [73] introduced an innovative approach to palmprint recognition that involves a two-stage process using a combination of weighted adaptive center symmetric local binary pattern (WACS-LBP) and weighted sparse representation-based classification (WSRC). Their methodology first implements WACS-LBP in an initial labeling stage to assign the test sample a limited set of feasible class labels. WSRC is then employed in the ensuing identification phase to determine the final class membership from this reduced label set. Core to their approach is the strategic conversion of the intrinsically complex complete classification problem into a more tractable task through substantial reduction of the number of output classes under consideration at each phase. [PERSON] et al. (2019) [74] proposed a multispectral palmprint identification method that incorporates Pascal coefficient multispectral local binary pattern (PCMLBP) and pyramid histogram orientation gradient (PHOG) descriptors. They performed two experimental procedures. In the first procedure, only the PCMLBP descriptor was used for feature extraction. In the second procedure, PCMLBP was combined with PHOG, resulting in an improved recognition rate. PCA was then used to reduce the dimensionality of the feature vectors. Finally, random sample linear discriminant analysis (LDA) was utilized for classification. [PERSON] et al. (2019) [75] introduced a palmprint identification approach that involves merging spiral features with LBP filters and selecting the optimal features using minimum redundancy maximum relevance (mRMR). This process starts by partitioning the palmprint image into smaller blocks, akin to meticulously examining individual pieces of a mosaic. Within each block, the researchers delve deeper, analyzing two crucial statistical descriptors: skewness and kurtosis. The Hamming distance is then applied to compute both inter-similarities between different blocks within the same palmprint and intra-similarities between corresponding blocks across different palmprints. [PERSON] and [PERSON] (2020) [76] proposed an approach for feature extraction in palmprint identification known as two-dimensional cochlear transform (2 DCT). This method was designed to efficiently capture distinctive palmprint features. To validate the efficacy of the method, the authors performed comprehensive analyses, including both theoretical and empirical assessments.