# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
For automated minutiae matching in these images, they are unable to be pre-processed successfully enough to obtain a satisfactory performance without a manual support because the matching of two prints is performed after they are manually aligned, and key points are manually marked. A handprint system requires modification to process the segments acquired without manual marks or alignment. Fingerprint images acquired with contact-based sensors are affected by scars on the finger and marks or dirt on the contacting surface, which limits the area of the usable image and hence the number of feature points that can be obtained from them. A handprint system incorporating fingers and palm can provide an increased area and more features, thereby increasing the potential to yield higher accuracy for identity verification. However, such a system has additional challenges in performing part-to-part registration and fusion of features or scores for verification. An increased area will also be accompanied by a larger number of spurious features. Handprint systems are still an ongoing area of research that address these challenges. In a handprint system, the palm print region shows many more minutiae (\(\times 10\) times) than fingerprints and is degraded by non-linear distortions. The hand structure and its increased surface area also pose challenges. Palm prints are very different from fingerprints, with additional creases, wrinkles, and palm lines, which tend to generate many spurious minutiae. Conventional fingerprint matching methods are therefore not very useful to implement automated systems that use palm prints. If the images exhibit geometrical transformations (Figure 1), degradation, and partial occlusion (as shown in Figure 2), they can yield large quality variations. A manual and careful examination is often needed for selecting a set of matching prints and features between them. The methods using palm prints are often not the same as those used with fingerprints. It is desirable from an implementation and interpretation of results perspective to have a common framework for processing both fingerprint and palm print images. Figure 1: Eight instances of hypothenar segments of a palm acquired from a single individual. Note that they show a range of rotational and scale changes for a single individual, and some parts of the segments show quality variation due to the skin-scanner contact pressure, such as thickness and dilution of ridges. There are several handprint-based recognition systems explored in contactless manner in the recent past [3; 4; 5]. A method using common feature representation for both finger and palm in contactless images was already presented using ridge information [1; 6; 7]. High-resolution contactless images were used to acquire ridge orientation information which was then converted into one-dimensional invariant higher order spectral features. However, in contact scenario, acquiring highly visible ridge information without degradation is still difficult unless high-quality prints are acquired, and therefore, alternative feature representation is urged. Further, there is no system (to the best of our knowledge) that accommodates both fingerprints and palm prints in a contact-based hand biometric system which suits both access control and forensic applications. The motivation of this work is one single algorithm for one hand when contact handprints are present in any application. This work is a step towards a common framework in that direction. In this context, a common framework would be desired to present: (i) a common feature representation; (ii) a common matching algorithm; (iii) robustness via features and matching approach for geometrical variations; and (iv) a strategy to reduce the spurious feature points that will arise from extremely degraded image segments. Minutiae and ridges are generally visible in the segments from a hand, and they can be detected to extract as common features.