## 7 Feature Extraction Approaches
### Line-Based Approaches  
Line-based methods focus on the identification and extraction of the main and local lines embedded in a palmprint in an image. These distinctive lines, which include both the prominent main ridges and the finer local ridges, serve as key features to facilitate accurate and efficient recognition processes. By strategically detecting and analyzing these lines, line-based techniques exploit the inherent uniqueness of the palm ridge pattern. The sophisticated ability of these methods to capture the intricate interplay between the major and minor lines enables the creation of highly informative palmprint templates, providing the basis for reliable and discriminatory recognition systems. This meticulous line-oriented approach adds an extra dimension of precision to palmprint recognition, making line-based methods an invaluable tool in the arsenal of biometric security mechanisms. [PERSON] et al. (2002) [PERSON] et al. (2002) proposed a novel approach for palmprint identification by exploiting the power of the Fourier transform to extract and represent spatial frequency features from palmprint images. Prior to feature extraction, the palmprint images are aligned and normalized. Then, the Fourier transform acts as a bridge, seamlessly transforming the palmprint image from the spatial domain, characterized by pixel intensities, into the frequency domain and detecting major lines in the contours. Finally, the retrieved features are used to guide a multi-level search in the database for the best match to the template. [PERSON] et al. (2008) [PERSON] et al. (2008) proposed a multi-feature-based technique for palmprint recognition that combines primary line (PL) and locality preserving projection (LPP) features. The technique involves extracting the main lines from the query image and comparing them with the main lines present in each image within the training set. Next, it creates a smaller training set consisting of the images with the highest similarity scores. Finally, the technique fuses the similarity scores of the main lines and the LPP features at the decision level and recognizes the query image in the smaller training set. [PERSON] et al. (2013) [PERSON] et al. (2013) introduced a novel approach for palmprint identification named histogram of oriented lines (HOL). This technique draws inspiration from the widely used histogram of oriented gradients (HOG) technique. Unlike HOG, which primarily  
Figure 9: Proposed taxonomy for palmprint feature extraction approaches. captures edge information, HOL delves deeper, specifically targeting and characterizing the prominent lines that define a palmprint's unique identity through the use of a series of Gabor filters with varying orientations or modified finite random transform (MFRAT). In the matching phase, they use the Euclidean distance as the similarity measure. [PERSON] et al. (2016) [46] unveiled a local line directional pattern (LLDP) descriptor based on line direction space for palmprint identification. To capture the line direction features, they used methods such as modified finite radon transform (MFRAT) and the real component of the Gabor filter. For the categorization process, they used Manhattan and Chi-square distances. [PERSON] et al. (2017) [47] proposed an intra-model palmprint recognition method that combines the characteristics of the major line and texture features. First, an elastic shape analysis framework was employed to explore the shape characteristics of the main line. Then, the texture information was explored using fractal analysis. Then, to improve the system accuracy, the important information from the various collected features of the main line shape and the texture pattern were merged. Finally, a random forest classifier was used to identify palmprints after combining the shape analysis-based and fractal-based features. [PERSON] et al. (2018) [48] proposed the HOG-SGF technique for palmprint identification, which combines histogram of oriented gradients (HOG) features with a steerable Gaussian filter (SGF).