## 7 Feature Extraction Approaches
### Deep Learning-Based Approaches

Table 6: _Cont._two tasks: palmprint recognition and gender categorization, respectively. Their ground-breaking study not only demonstrated unprecedented performance in biometric recognition but also needed to validate the notion that integrating gender information could improve the accuracy of palmprint identification. To further test this concept, they created two Boost CNN networks, BoostNet-Sequential and BoostNet-Parallel, with the goal of combining the strengths of palmprint recognition and gender categorization. [PERSON] et al. (2019) [82] presented PalmNet, an innovative CNN architecture. In particular, their unsupervised training eliminates the need for class labels. They also proposed a novel Gabor-based technique that uses PCA to refine adaptive filters within the CNN, thereby improving its specificity for palmprint recognition. [PERSON] and [PERSON] (2020) [83] presented a novel approach to improve multi-scenario palmprint recognition using a versatile framework. It uses deep convolutional networks (DC-NNs), termed deep discriminative representation (DDR), to learn effective features. These features work well for palmprint recognition under various conditions. The key innovation is to train DC-NNs to extract discriminative features from palmprints that include global abstract and local compact attributes. This framework remains effective even with limited training data, providing a new avenue for advancing palmprint recognition in diverse scenarios. [PERSON] and [PERSON] (2020) [24] presented a robust and versatile deep learning framework for contactless palmprint identification. Their approach uses a fully convolutional network to generate complex residual features (RFNs), which improves accuracy and generalizability. A distinctive feature is the utilization of a soft-shifted triplet loss function, which enhances the learning of discriminating the features of palmprints. Additionally, they incorporated a contactless palm detector, customized and trained utilizing the promter CNN model, for effective detection of palmprint regions across diverse backgrounds. [PERSON] et al. (2021) [84] introduced an end-to-end deep hashing network tailored for few-shot contactless palmprint identification, termed the similarity metric hashing network (SMHNet). Their framework integrates a structural similarity index (SSIM) module to elicit multi-scale representations encoding both holistic topology and localized texture details. A composite SSIM loss function alongside distance metrics supervises the training process for enhanced inter-class separability. Additionally, a hashing unit learns binary compact codes optimized for efficient storage and fast retrieval demonstrated significant improvement in few-shot recognition of palmprints. [PERSON] et al. (2022) [41] introduced a progressive target distribution loss (PTD Loss) function, which is tailored to minimize the gap between positive cross-device sample affinities and negative within-device sample relations. Additionally, the authors established a new cross-device palmprint identification dataset compositing color images sourced from multiple capture platforms. [PERSON] and [PERSON] (2022) [85] developed a deep metric learning paradigm designed for open-set contactless palmprint identification termed weight-based meta-metric learning (W2 ML). Their framework strategically partitions the dataset into training and testing subsets without overlap between the two phases. The training set is further divided into multiple tasks, each comprising a support set for representation learning and a query set for few-shot generalization assessment akin to meta-learning by aggregating support sets from the task-specific subspaces into consolidated positive and negative meta-sets. The model is then trained using set-based distances between them. Additionally, hard prototype mining and weighting further enhances discrimination by identifying and prioritizing the most informative samples within each meta-set (from positive and negative).