Large-scale face images retrieval: A transform coding approach

Huge efforts have been devoted to face recognition technology and remarkable results, noticed. Such advances will provide us the possibility to build a new generation of search engine: persons photo fetching. It is a real computing challenge to find a person from a very large or extremely large database which might hold face images of millions or hundred millions of people. A candidate solution is to use partial information (signature) about all the face images for search, making the retrieval speed approximately proportional to the size of a signature image. In this paper we will investigate a totally new way to compress the signature images based on the observation that the face signature images and the query images are highly correlated if they are from the same individual. The face signature image can be greatly compressed (one or two orders of magnitude improvement) by use of knowledge of the query images. We can expect the new compression algorithm to speed up face search 10 to 100 times. The challenge is that query images are not available when we compress their signature image. Our approach is to transfer the face search problem into the so-called “Wyner-Ziv Coding” problem, which could give the same compression efficiency even if the query images are not available until we decompress signature images. A practical compression scheme based on LDPC codes is developed to compress and retrieve face signature images.