Speeding up the knowledge-based deblocking method for efficient forensic analysis

Identifying individuals in evidence images (e.g. child sexual abuse and masked gunmen), where their faces are covered or obstructed, is a challenging task. Skin mark patterns and blood vessel patterns have been proposed as biometrics to overcome this challenge, but their clarity depends on the quality of evidence images. However, evidence images are very likely compressed by the JPEG method, which is widely installed in digital cameras. To remove blocking artifacts in skin images and restore the original clarity for forensic analysis, a knowledge-based deblocking method, which replaces compressed blocks in evidence images with uncompressed blocks from a large skin image database, was proposed. Experimental results demonstrated that this method is effective and performs better than other deblocking methods that were designed for generic images. The search for optimal uncompressed blocks in a large skin image database is computationally demanding. Ideally, this computational burden should be reduced since even in one single case, the number of evidence images can be numerous. This paper first studies statistical characteristics of skin images. Making use of this information, hash functions, bitwise ℓ1-minimization, and a parallel scheme were developed to speed up the knowledge-based deblocking method. Experimental results demonstrate that the proposed computational techniques speed up the knowledge-based deblocking method more than 150% on average.