Vein pattern recognition based on RGB images using Monte Carlo simulation and ridge tracking

In forensic investigations, images of evidence can often be obtained from crimes such as child pornography and masked violent riots. However, identifying criminals is usually very difficult and sometimes impossible because these images usually contain skin of body parts, while their faces and other commonly used biometrics are unavailable. Vein patterns are a potential biometric to solve this problem. Traditional systems use near-infrared (NIR) imaging technologies to obtain vein patterns, which cannot be applied to forensic analysis since only RGB images are available. However, veins are unobservable in RGB images. In this paper, a comprehensive scheme including a vein uncovering algorithm, a vein extraction algorithm, and a vein pattern matching algorithm is presented. Based on the Monte Carlo (MC) simulation of light transmission in a skin optical model, physical parameters corresponding to different skin colors are obtained, and vein patterns are uncovered from the parameter distribution images. After preprocessing with cubic convolution and Gabor filtering, vein lines are extracted based on ridge tracking. Local gradient orientation and the geometric direction of veins are utilized to guarantee the correct tracking direction. Hessian-based Frangi filters are adopted to locate potential veins. In the matching step, effective minutiae are extracted to represent the topology of vein patterns. A modified coherent point drift (CPD) algorithm is proposed utilizing coordinates, Gabor energy values, and curvatures of minutiae to match vein patterns. Comprehensive experiments were carried out to evaluate the proposed three algorithms. Experimental results show the superiority of the proposed algorithms to various state-of-the-art methods.