Indexing fingerprint database with minutiae based coaxial Gaussian track code and quantized lookup table

Large scale adaptation of a fingerprint based recognition system results in expansion of its database which increases the cost of identification and degrades the system performance. The paper proposes an efficient indexing technique that can scatter the effect of database escalation and maintains the system performance. It has proposed a fixed length feature vector built from each minutia, known as Coaxial Gaussian Track Code (CGTC). The proposed technique inserts feature vector into a Quantized Lookup Table (QLT) only once. As a result, it reduces both computational and memory costs. Since minutiae of all fingerprint images in the database are found to be well distributed in the quantized lookup table, it does not need rehashing. Experiments have been conducted over three fingerprint databases viz. FVC2002, FVC2004 and IITK-Sel500FP containing fingerprints from 100, 100 and 500 subjects respectively. Results have proven the superiority of the proposed indexing technique against well known geometric based indexing techniques.