Supervised Hashing for Retrieval of Multimodal Biometric Data

T. A. Sumesh, Vinay Namboodiri, Phalguni Gupta

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

32 Downloads (Pure)


Biometric systems commonly utilize multi-biometric approaches where a person is verified or identified based on multiple biometric traits. However, requiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. These are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing techniques that are available for obtaining retrieval. We specifically based on our analysis recommend the use of supervised hashing techniques over deep learned features as a possible common technique to solve this problem. Our investigation includes a comparison of some of the supervised and unsupervised methods viz. Principal Component Analysis (PCA), Locality Sensitive Hashing (LSH), Locality-sensitive binary codes from shift-invariant kernels (SKLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint for a number of standard datasets. The main technical contributions through this work are as follows: (a) Proposing Siamese network based deep learned feature extraction method (b) Analysis of common feature extraction techniques for multiple biometrics as to a reduced feature space representation (c) Advocating the use of supervised hashing for obtaining a compact feature representation across different biometrics traits. (d) Analysis of the performance of deep representations against shallow representations in a practical reduced feature representation framework. Through experimentation with multiple biometrics traits, feature representations, and hashing techniques, we can conclude that current deep learned features when retrieved using supervised hashing can be a standard pipeline adopted for most unimodal and multimodal biometric identification tasks.

Original languageEnglish
Title of host publicationComputer Vision Applications - 3rd Workshop, WCVA 2018, held in Conjunction with ICVGIP 2018, Revised Selected Papers
EditorsChetan Arora, Kaushik Mitra
PublisherSpringer, Singapore
Number of pages13
ISBN (Print)9789811513862
Publication statusE-pub ahead of print - 15 Nov 2019
Event3rd Workshop on Computer Vision Applications, WCVA 2018, held in conjunction with the 11th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2018 - Hyderabad, India
Duration: 18 Dec 201818 Dec 2018

Publication series

NameCommunications in Computer and Information Science
Volume1019 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference3rd Workshop on Computer Vision Applications, WCVA 2018, held in conjunction with the 11th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2018


  • Biometric systems
  • Supervised hashing

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)


Dive into the research topics of 'Supervised Hashing for Retrieval of Multimodal Biometric Data'. Together they form a unique fingerprint.

Cite this