A Granular Level Feature Extraction Approach to Construct HR Image for Forensic Biometrics Using Small Training DataSet

Abstract

In forensic biometrics old x-ray images are often used for identification and verification. The number of homicide cases has increased manyfold along with population growth. Forensic odontology is a less expensive method to solve these cases. In forensic odontology, the old dental x-ray images of a victim are used as ante mortem data to compare the pattern for identification. The success of this post mortem biometrics process totally depends on the brain perception of forensic odontologist. Low resolution (LR) image may create human brain perception error at the time of making decision both in case of disease diagnosis and forensic biometrics. In such context, a software solution could help to reconstruct high resolution (HR) image from LR medical x-ray image. However, methods like convolution neural network (CNN) require high volume of training images to reconstruct HR images. Unfortunately, the available medical x-ray image repository does not offer a large volume of training dataset. This work aims to overcome this data related issue and presents a granular level feature based HR grayscale medical xray image reconstruction mechanism from LR image. This method uses machine learning for HR image reconstruction. The proposed granular level feature extraction method generates adequate amount of training set from limited amount of training image sets. These granular level features contain the influence of neighboring points separately and its direction. These features are highly immune to noises and preserve important properties like edges. Polynomial regression model is used for HR value reconstruction. This image reconstruction provides satisfactory results for image database like dental radiographs datasets and the average structural similarity index (SSIM) metric reaches 0.9326.

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