# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 2 Related Work
### Palm Prints
#### 2.2.2 Match between Partial vs. Full Palm Prints  
In partial-to-full palm print matching, the live-scan prints are acquired from a high-resolution scanner. The prints have similar geometric and photometric constraints as those in contactless images, and therefore, they need an alignment process before matching. Apart from minutiae-based matching, texture- and transform-based approaches were in practice with contact-based palm prints. However, these approaches have to address the image alignment problem [29]. Since partial palm prints have fewer structural features, image-based (texture) features were exploited in these systems. For an instance, the work in [30] used Gabor filter features by combining multiple regions of the palm features to give a good solution during the application of access control. Similarly, [PERSON] et al. [22] exploited scale-invariant feature transformation (SIFT) descriptors in high-resolution contact palm prints where the correspondences between key points are estimated by the Euclidean distance of the descriptors. The work in [29] used both iterative closest points (ICP) and palm-line orientation information for the processes of image alignment and matching, respectively. However, because of having noise in the palm line and using orientation features, the ICP algorithm cannot guarantee the accurate alignment of correspondence image pairs.  
It is outlined from the literature that several contact-based palm prints used line and orientation-based features in the early days [31]. Line or edge detectors were used to extract such features using differences in Gaussian filters (DoGs) [32], Gabor filters [33], and Radon transform filters [34]. However, they have certain limitations. For instance, the DoG-based method is sensitive to noise and illumination. Since partial palm prints have fewer structural features and are full of lines, textures and orientation-based features have also been exploited using orientation-based descriptors, such as palmcode, competitive code [35], fusion code, the robust line orientation code method (RLOC) [36], and double orientation code (DOC) [37]. However, it is noted that during the process of orientation extraction, none of the filters show the same orientation as the palm print line [38]. Further, the CompCode approach is based on the Gabor features [39; 40]. To use the Gabor filters, the images should be aligned or a key point should be detected for the initial alignment of the pair of matching images. The region of interest should already be determined. This can be applied for all the variants of CompCode, such as RLOC, DOC, etc. This is the main issue in the filter-based texture features, along with the computational cost [41].  
The work in [30] combined multiple regions of the palm features with Gabor filters. However, this method solely depends on manual segmentation of the palm into several regions and used regional fusion of palm print segments. Further, using Gabor features in a larger surface area can have higher computational cost. Therefore, the feature encoding for handprints needs to be designed to incorporate the automated image alignment (for geometrical transformations) and less computational cost. Further, the feature representation of contactless palm print images cannot be directly used in contact images due to low image quality and low resolution because contactless palm print or handprint approaches use multiview image [42; 43] and multiresolution [44] image capture with multimodal approaches [45; 46]. These strategies help performance-related issues arising from image quality and low resolution. This leads towards a fair way of adopting minutiae-based invariant representation rather than image-based structural features.