## 2 Related Work
### Feature Extraction and Matching  
So far, many approaches have been proposed for feature extraction and matching tasks that can be categorized as hand-crafted and deep learning descriptors. Comprehensive studies of feature extraction approaches are provided in [26; 27; 28; 29], which are divided into three main categories: holistic-based, local-based, and hybrid methods.  
Among the available local feature-based approaches, some have been shown to perform effectively on low-resolution palmprint images. To extract principal lines (PriLine), [PERSON] et al. [30] applied a Radon filter as an edge detector and provided two levels of authentication in order to increase the accuracy. Moreover, there are some coding-based methods, including competitive code (CompCode) [31], double orientation code (DOC) [32], and extended binary orientation co-occurrence vector (E-BOCV) [33], where a bank of Gabor filters was applied to represent the orientation features of palmprint images. The local line directional patterns (LLDP) [34], as a local texture descriptor, also extract the local line directional features by convolving the line filters with a palmprint image such as local line directional patterns-modified finite radon transform (LLDP-MFRAT) and LLDP-Gabor descriptors.  
Although there are numerous studies in this field [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49], sensitivity to illumination, distortion, translation, and rotation variances still remains as a main challenge [6]. Furthermore, some local feature-based descriptors in hand-crafted techniques highly depend on the input image resolution, leading to dramatic increment in the sensor cost [28].  
Considering these issues, instead of designing algorithms for hand-crafted descriptors, which can be very time-consuming, feature extraction can be automatically obtained by deep learning descriptors [50]. This automatic feature mapping results in high-level features which can hardly be obtained via hand-crafted approaches [51]. Another advantage of deep learning descriptors over the hand-crafted ones is that they can be trained with a large number of inputs to become robust against illumination, distortion, translation, and rotation variances. Automatic feature engineering exploits the hierarchical architecture to effectively learn complex models that are highly appropriate for unconstrained conditions (e.g., image background or palm position in the input image) [51].  
Motivated by the aforementioned considerations, CNN as the most notable deep learning descriptor has attracted researchers' attention in recent years [52; 53; 54; 55; 56]. The work of [PERSON] and [PERSON] [52] explored the matching possibility of left and right palmprint images, and several algorithms were investigated; the best results were obtained by CNN. Although they focus on an interesting topic, their method error rate is especially high for forensic applications. The authors of [5] used a principal component analysis network (PCANet) model [57] in feature extraction and an SVM classifier for palmprint identification. In this way, each spectral band was represented by features extracted by a deep learning technique. Since they used the ROI extraction method described in [14], their approach lacks robustness against illumination, distortion, translation, and rotation variances in input images. For the same reason, the approaches in References [53; 54] are not robust against input image deformation. [PERSON] et al. [55] exploited a scattering network (a convolutional network where the architecture and filters are predefined wavelet transforms) for feature extraction, but no learning was involved in the ROI extraction. In [56], the input images were resized into \(28\times 28\) pixel images without applying ROI extraction techniques, which led to missing important features.