A Study of Distinctiveness of Skin Texture for Forensic Applications Through Comparison With Blood Vessels

Skin texture without obvious features is different from other hard biometrics on the skin, such as fingerprints and palmprints. Skin texture gives an impression that it is not distinctive like other soft biometric traits. It was proposed for personal identification a decade ago but did not draw attention from the biometric community, partially due to the success of other biometric technologies for commercial applications. However, in some forensic cases, e.g., identifying masked terrorists in images, skin texture may be the only option. Faces, tattoos, and skin marks are not always available for identification. To address these forensic needs, researchers have recently attempted to visualize blood vessels hidden in color images. Their performance is highly sensitive to image quality. Skin texture that is easily captured even in low-resolution images, such as that of the forearm skin, is suitable for these forensic applications. To study the distinctiveness of low-resolution skin texture, in this paper, an algorithm composed of a positive sample generation scheme, dynamic and directional grids, a large feature set generation scheme, and partial least squares regression has been proposed. More than 6300 inner forearm and thigh images collected from a laboratory environment and from the internet with large pose, viewpoint, and illumination variations were employed in this paper. The proposed algorithm was compared with the state-of-the-art texture recognition methods, and skin texture was compared with blood vessels, a hard biometric trait, extracted from color and infrared images. The results showed that the proposed algorithm performed significantly better than did the texture recognition methods, and skin texture outperformed blood vessels in all of the experiments, achieving encouraging performance.