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Identifying individuals 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 pigmented skin marks to identify a suspect in evidence images. Their expert opinions were challenged, partially because the blocking artifacts generated by the standard JPEG algorithm adversely affect the visibility of the small skin marks. In addition to this case, a huge amount of JPEG-compressed child pornography is posted online every day. Although many methods have been developed to remove blocking artifacts, they are ineffective for our target application. In this paper, a knowledge-based (KB) approach, which simultaneously removes JPEG blocking artifacts, and recovers skin features, is proposed. Given a training set containing both original and compressed skin images, the relationship between original blocks and compressed blocks can be established. This prior information is used to infer the original blocks of compressed evidence images. A Markov-model-based algorithm and a faster one-pass algorithm were developed to make inference, and a block synthesis algorithm was developed to handle the cases where compressed blocks are not contained in the training set. An indexing mechanism was also proposed to deal with large datasets efficiently. Extensive experiments were conducted on images with different characteristics and compression ratios. Both subjective and objective evaluations demonstrated that the KB approach is more effective than other methods. In summary, the KB approach is capable of removing blocking artifacts to recover useful skin features.