Vein pattern visualization through multiple mapping models and local parameter estimation for forensic investigation

Abstract

Forensic investigation methods based on some human traits, including fingerprint, face, and palmprint, have been developed significantly, but some major aspects of particular crimes such as child pornography still lack of notable research efforts. Unlike common forensic identification methods, techniques for identifying criminals in child pornographic images should be developed based on partial non-facial skin observable in the images because criminals always hide their faces. Few methods published recently have shown the potential of vein patterns visualized from color images as a criminal and victim identification tool.

However, these methods have two weaknesses: 1) they use single model to visualize vein patterns hidden in color images, which neglects the diversity of skin properties and 2) even though their parameters are determined automatically by an optimization, they do not adapt to fit local image characteristics. To address these weaknesses, this paper proposes an algorithm composed of a bank of mapping models which transform color images to near infrared (NIR) images for visualizing vein patterns and a local parameter estimation scheme for handling different image characteristics in different regions. Imbalanced data regression is also used to systematically construct the model bank. The proposed algorithm is examined and compared with the previous methods on a database of 920 thigh images from 230 subjects. It outperforms the previous methods.