FKPIndexNet: An efficient learning framework for finger-knuckle-print database indexing to boost identification

Abstract

This paper addresses the problem of identification in the Finger-knuckle-print (FKP) databases. Identification determines the identity of a query of the FKP sample. It involves finding the most similar sample in the database by comparing the query FKP with all the templates stored in the database. It is a computationally expensive process that demands huge time for large databases. A technique is required that can reduce the search space and limits the number of comparisons to boost the identification process. Such a technique is called indexing. It devises a fixed size small candidate list for a given FKP sample in constant time for searching. The paper proposes FKPIndexNet that learns similarity-preserving hash codes for generating an index table. It employs a specialized autoencoder network to learn feature embeddings such that they have high intra-class and low inter-class similarity. The proposed technique is examined on two publicly available FKP databases viz., PolyU-FKP and IITD-FKP. Experimental results show that the proposed method achieves 100% hit rate at a penetration rate of only 3.42% for PolyU-FKP database and 0.32% for IITD FKP database, respectively. This implies that for a query FKP sample, to get a true match with 100% confidence, only 3.42% and 0.32% of the PolyU-FKP and IITD FKP database needs to be compared, respectively. Results and analysis demonstrate the superiority of the proposed technique compared to other state-of-the-art approaches.