FKPIndexNet: An Efficient Learning Framework for Finger-Knuckle-Print Database Indexing to Boost Identification

HARI MOHAN PANDEY*, Geetika Arora, Avantika Singh, Aditya Nigam, Kamlesh Tiwari

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

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.
Original languageEnglish
Article number108028
JournalKnowledge-Based Systems
Volume239
Early online date28 Dec 2021
DOIs
Publication statusE-pub ahead of print - 28 Dec 2021

Keywords

  • Finger-Knuckle-print
  • Identification
  • Indexing
  • Biometrics
  • Autoencoder

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