On-chip face recognition system design with memristive Hierarchical Temporal Memory

Timur Ibrayev, Ulan Myrzakhan, Olga Krestinskaya, Aidana Irmanova, Alex Pappachen James

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Hierarchical Temporal Memory is a new machine learning algorithm intended to mimic the working principle of the neocortex, part of the human brain, responsible for learning, classification, and making predictions. Although many works illustrate its effectiveness as a software algorithm, hardware design for HTM remains an open research problem. Hence, this work proposes an architecture for HTM Spatial Pooler and Temporal Memory with learning mechanism, which creates a single image for each class based on important and unimportant features of all images in the training set. In turn, the reduction in the number of templates within database reduces the memory requirements and increases the processing speed. Moreover, face recognition analysis indicates that for a large number of training images, the proposed design provides higher accuracy results (83.5%) compared to only Spatial Pooler design presented in the previous works.

Original languageEnglish
Pages (from-to)1393-1402
Number of pages10
JournalJournal of Intelligent and Fuzzy Systems
Volume34
Issue number3
DOIs
Publication statusPublished - Jan 1 2018
Externally publishedYes

Keywords

  • face recognition
  • HTM
  • memristor
  • spatial pooler
  • temporal memory

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

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