Bioinspired memory model for HTM face recognition

2016

Closed access Metadata (CSL-JSON)
DOI 10.1109/icacci.2016.7732265
Data confidence 70 medium
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Abstract

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.