# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 2 Related Work
### Palm Prints
#### 2.2.3 Latent Prints  
Minutiae are a common feature used for palm print identification [47], while they played a role in latent prints, and they can be extracted automatically [48]. However, the presence of a load of minutiae is a major issue in latent prints due to the extreme lower image quality similar to Figure 2, which requires a larger processing time for handling them. Another issue is that there may be an overlap between the pair of prints during the match, which can lead to diminishing the system's efficiency. Further, manual marking of the minutiae is also practiced in latent prints. In the literature, [PERSON] et al. [18] used radial triangulation of minutiae for alignment of images. This yields estimation of the likelihood ratio of the matching images, rather than providing an actual match decision. [PERSON] et al. [27] used clusters of minutiae-based on ridge period descriptors and orientation of ridges to overcome the issue of an enormous set of minutiae present in the latent prints. However, fake points and print overlap remain in the prints, reducing matching performance. The work in [49] used a triangular sign and a minutiae vicinity descriptor (which is a counter of neighboring minutiae in the extended triangle set) along with the process of manual minutiae marking to overcome the spurious minutiae and noise. A recent work exploited a deep-learning-based method for feature extraction [50]. However, deep learning methods are in early stages in latent domains and need more images for training to avoid over-fitting. It is summarized that the existing work on the latent prints still holds the alignment issue and practice of manual marking of the minutiae. Since latent prints can be obtained from any part of the hand, it is not clear in the literature how geometrical variations in the images are normalized without any manual support. Further, the use of minutiae triangles or polygon-based minutiae matching is comparatively less practiced with latent prints. Further, the majority of existing contact-based biometric matches used either fingerprints or palm prints for a single instance or in a separate platform and did not evaluate both types of impressions in a combined single framework. If there is an algorithm to handle any print from a hand, then the civilian applications and the forensic domain will obtain benefit from being able to process the prints from several parts of a hand surface. In a similar way, multiple contact-based prints from a hand also would obtain benefit by using a single algorithm. Since minutiae and ridges are common features for both prints, there is a possibility of utilizing all prints from a hand in a single framework. If an algorithm based on the minutiae is proposed for these prints, it should support (i) geometrical normalization of two images; (ii) elimination of manual marking of minutiae; and (iii) elimination of spurious minutiae. In this context, the Delaunay triangulation and graph-based algorithm are proposed in this paper as it would be advantageous in contact prints when there is no information about where the prints from a hand are obtained. Delaunay triangles have lower computational complexity compared to the other minutiae triangles and quadruplets during matching, which is proportional to the time complexity. Combining different impressions from a hand using a single framework and introducing a new algorithm using minutiae or minutiae-like key points can be a new approach. When severe distortion in contact prints affects the presence of minutiae, minutiae-like points, such as pores and palm-line intersection points, can also be utilized in high-resolution partial and latent prints during cross-matching modality. Since lines, wrinkles, and distortion generate a large number of spurious minutiae, the algorithm should carefully handle them and automatically align the prints before matching. Further, it should preserve the invariant nature for geometrical variations. The proposed approach is summarized with three alternative strategies below:  
1.