Restoring Blood Vessel Patterns from JPEG Compressed Skin Images for Forensic Analysis

The recent development of forensic methods makes criminal and victim identification based on blood vessel patterns hidden in color images possible. The performance of these methods highly depends on the quality of input images. The JPEG method, being the most popular image compression method, generates blocking artifacts, which seriously degrade image quality and make uncovering blood vessels difficult. This paper proposes an algorithm to restore blood vessel patterns hidden in JPEG compressed skin images. An analysis is first performed to identify critical discrete cosine transform (DCT) coefficients that control the quality of blood vessel patterns. According to the analysis, an algorithm is designed to restore these coefficients by exploiting DCT coefficients in different blocks and channels of JPEG compressed images. A blood vessel pattern matching method and a database with 978 images are employed to evaluate the effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed algorithm can effectively alleviate blocking artifacts and restore blood vessel patterns for matching and that it outperforms the knowledge-based deblocking method, which is specially designed to restore skin images.