Learning Compact Binary Codes for Hash-Based Fingerprint Indexing

Compact binary codes can in general improve the speed of searches in large-scale applications. Although fingerprint retrieval was studied extensively with real-valued features, only few strategies are available for search in Hamming space. In this paper, we propose a theoretical framework for systematically learning compact binary hash codes and develop an integrative approach to hash-based fingerprint indexing. Specifically, we build on the popular minutiae cylinder code (MCC) and are inspired by observing that the MCC bit-based representation is bit-correlated. Accordingly, we apply the theory of Markov random field to model bit correlations in MCC. This enables us to learn hash bits from a generalized linear model whose maximum likelihood estimates can be conveniently obtained using established algorithms. We further design a hierarchical fingerprint indexing scheme for binary hash codes. Under the new framework, the code length can be significantly reduced from 384 to 24 bits for each minutiae representation. Statistical experiments on public fingerprint databases demonstrate that our proposed approach can significantly improve the search accuracy of the benchmark MCC-based indexing scheme. The binary hash codes can achieve a significant search speedup compared with the MCC bit-based representation.