Bioinspired memory model for HTM face recognition

Olga Krestinskaya, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Inspired from the working principle of human memory, we propose a new algorithm for storing HTM features detected from images. The resulting features from the training set require lower memory than existing HTM training set. The proposed features are tested in a face recognition problem using the benchmark AR dataset. The simulation results show that the proposed algorithm gives higher face recognition accuracy, in comparison to the conventional methods.
Original languageEnglish (US)
Title of host publication2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781509020287
StatePublished - Nov 2 2016
Externally publishedYes


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