Quality assessment of degraded Palmprints using enhancement filters

Abstract

Image enhancement in the pre-processing stage of biometric systems is a crucial task in image analysis. Image degradation significantly impacts the biometric system’s performance, which occurs during biometric image capturing, and demands an appropriate enhancement technique. Generally, biometric images are mixed with full of noise and deformation due to the image capturing process, pressure with sensor surface, and photometric transformations. Therefore, these systems highly demand pure discriminative features for identification, and the system’s performance heavily depends on such quality features. Hence, enhancement techniques are typically applied in captured images before go into the feature extraction stage in any biometrics recognition pipeline. In palmprint biometrics, contact-based palmprints consist of several ridges, creases, skin wrinkles, and palm lines, leading to several spurious minutiae during feature extraction. Therefore, selecting an appropriate enhancement technique to make them smooth becomes a significant task. The feature extraction process necessitates a completely pre-processed image to locate key features, which significantly influences the identification performance. Thus, the palmprint system’s performance can be enhanced by exploiting competent enhancement filters. Palmprints have reported a lack of novelty in enhancement techniques rather than more centering on feature encoding and matching techniques. Some enhancement techniques in fingerprints were adopted for palmprints in the past. However, there is no clear evidence of their impact on image quality, and to what extent they affect the quality in specific applications. Further, frequency level filters such as the Gabor and Fourier transforms exploited in fingerprints would not be practically feasible for palmprints due to the computational cost for a larger surface area. Thus, it opens an investigation for utilising enhancement techniques in degraded palmprints in a different direction. This work delves into a preliminary investigation of the usage of existing enhancement techniques utilised for pre-processing of contact fingerprint images and biomedical images. Several enhancement filters were experimented on severely degraded palmprints, and the image quality was measured using image quality metrics. The High-boost filter comparatively performed better peak-signal-to-noise ratio, while other filters affected the image quality. The experiment is further extended to compare the identification performance of degraded palmprints in the presence and absence of enhanced images. The results reveal that the enhanced images with the filter that has the highest peak signal-to-noise ratio (High boost filter) only show an increased genuine accept rate compared to the ground truth value. The High-boost filter slightly decreases the system’s equal error rate, indicating the potential of exploiting a pre-enhancement technique on degraded prints with an appropriate filter without compromising the raw image quality. Optimised enhancement techniques could be another initiative for addressing the severity of image degradation in contact handprints. Doing so they could be successfully exploited in civilian applications like access control along with other applications. Further, utilising appropriate enhancement filters for degraded palmprints can enhance the existing palmprint system’s performance in forensics, and make it more reliable for legal outcomes.