## 7 Feature Extraction Approaches
### Local Direction Encoding-Based Approaches  
Methods based on local orientation coding focus on extracting and encoding the prevailing orientation information inherent in each pixel of a palmprint image. These methods differ from conventional techniques by focusing on the underlying directional characteristics of the ridge patterns, capturing the nuanced variations in orientation that contribute to the uniqueness of each palmprint. By extracting dominant directions at a local level, these methods reveal the intricate minutiae of the palm surface, going beyond traditional ridge-based representations. This wealth of directional information is then distilled into compact and informative bitwise codes, creating a powerful and discriminative coding scheme. These techniques effectively balance the precision of directional cues with the efficiency of compact coding, resulting in improved recognition performance. In the landscape of biometric authentication, these methods carve out a distinctive niche, offering a fusion of geometric understanding and efficient data representation that contributes to the development of accurate and reliable identity verification systems. [PERSON] and [PERSON] (2004) [59] proposed a palmprint recognition method based on real Gabor function (RGF) filtering. The process starts by normalizing palmprint images. Subsequently, these normalized images are subjected to multi-channel filtering using a set of RGF filters. Distinctive features, referred to as PalmCode, are computed within multiple overlapping concentric bands using each of these filtered images. [PERSON] et al. (2006) [60] proposed a feature-level coding method for palmprint recognition. First, palmprint image features are extracted utilizing a bank of elliptic Gabor filters. Then, a feature-level fusion technique is offered to create a single feature known as the fusion code. The normalized Hamming distance between two fusion codes is then used to determine their similarity. Finally, a dynamic threshold is applied for final judgments. [PERSON] et al. [61] proposed a multi-scale feature encoding strategy fusing contourlet transforms (CTs) and non-subsampled contourlet transforms (NSCTs) for palmprint recognition. This method aims to jointly capture localized texture details alongside global features within palmprint imagery, representing them as a compact and fixed-length palm code. The iterated directional filter banks are introduced to divide the two-dimensional spectrum into small slices. The feature vector is then formed by computing the block-wise directional energy in the transform domains. Finally, for matching, normalized Euclidean distances between vectorial codes quantify palmprint identity similarity. [PERSON] et al. (2012) [62] proposed a novel approach for palmprint identification based on local direction encoding. Specifically, the authors augment native BOCV descriptors with additional \"fragile bits\" constituting noise-sensitive activations to derive extended BOCV (E-BOCV). A consolidated similarity metric was obtained by synergizing fragile pattern distance (FPD) with Hamming distances to capture the mismatches between two code maps.