Image Retrieval in Forensics : Application to Tattoo Image Database

The continuing growth of and increasing dependence on forensic image databases require fast and reliable image matching and retrieval techniques. We present a content-based image retrieval (CBIR) system for a particular forensic image database, namely a large collection of tattoo images. The system employs a local point descriptor to represent images, and, given a query tattoo image, it retrieves near-duplicate images from a large-scale database. Despite the high retrieval accuracy of the system, the performance heavily relies on the quality of query images. If query images are of low quality, features extracted from the query are noisy and not sufficiently discriminative, resulting in poor retrieval performance. In this paper, we improve the robustness of the system, especially for low quality query images, which, consequently, improves the overall retrieval performance. We introduce effective weighting schemes for matching local keypoints as well as utilize metadata to further improve the retrieval performance. Experimental results on a database of 100,000 images show that our system has excellent retrieval performance with a top-20 retrieval accuracy of 90.5%.