A knowledge-based algorithm to remove blocking artifacts in skin images for forensic analysis

Identifying criminals and victims in evidence images, where their faces are covered or obstructed, is a challenging task. In the legal case, United States v. Michael Joseph Pepe (2008), Craft and Kong, who served as expert witnesses, used nevi to identify a pedophile in evidence images. Their expert opinions were challenged, partially because the blocking artifacts generated by the standard JPEG algorithm adversely affected the visibility of the nevi. In addition to this case, a huge amount of JPEG compressed child pornography is posted on-line every day. Although many methods have been proposed to remove blocking artifacts, they are ineffective for our target application. In this paper, a knowledge-based algorithm, which simultaneously removes JPEG blocking artifacts, and recovers skin features, is proposed. Given a training dataset which contains original and compressed skin images, the relationship between original blocks and compressed blocks can be established. This prior information is used to infer original blocks of compressed evidence images. An indexing mechanism is also proposed to deal with large datasets efficiently. Extensive experiments are conducted on images with different characteristics and compression ratios. Both visual comparison and subjective evaluation demonstrate that the proposed algorithm is more effective than other methods.