Memristive Threshold Logic Face Recognition
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Author(s)
Maan, Akshay
S. Kumar, Dinesh
James, Alex Pappachen
Griffith University Author(s)
Year published
2014
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This paper presents a face recognition method implemented using reconfigurable network of memristive threshold logic cells that can be practically realised in a secondary plane to the pixel arrays. Among the most distinguishing features of the presented system are a) an early detection and storage of only the relevant information directly from the sensors, b) a parallel, scalable information storage and detection architecture in hardware, as opposed to an algorithmic approach, and c) a fast and robust face recognition system. The threshold logic cell is inspired from a simplistic cortical neuron model that has multiple inputs ...
View more >This paper presents a face recognition method implemented using reconfigurable network of memristive threshold logic cells that can be practically realised in a secondary plane to the pixel arrays. Among the most distinguishing features of the presented system are a) an early detection and storage of only the relevant information directly from the sensors, b) a parallel, scalable information storage and detection architecture in hardware, as opposed to an algorithmic approach, and c) a fast and robust face recognition system. The threshold logic cell is inspired from a simplistic cortical neuron model that has multiple inputs with corresponding input memristors and one binary output. These cells when used with a set of input memristors are able to detect significant pixel variations in the incoming video frame and memorize the output template depending on the logic of selection of the resistor values. The implemented face recognition circuit shows small chip area, low power dissipation and ability to scale the networks with increase in image resolutions.
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View more >This paper presents a face recognition method implemented using reconfigurable network of memristive threshold logic cells that can be practically realised in a secondary plane to the pixel arrays. Among the most distinguishing features of the presented system are a) an early detection and storage of only the relevant information directly from the sensors, b) a parallel, scalable information storage and detection architecture in hardware, as opposed to an algorithmic approach, and c) a fast and robust face recognition system. The threshold logic cell is inspired from a simplistic cortical neuron model that has multiple inputs with corresponding input memristors and one binary output. These cells when used with a set of input memristors are able to detect significant pixel variations in the incoming video frame and memorize the output template depending on the logic of selection of the resistor values. The implemented face recognition circuit shows small chip area, low power dissipation and ability to scale the networks with increase in image resolutions.
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Conference Title
Memristive Threshold Logic Face Recognition
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Copyright Statement
© The Author(s) 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License (http://creativecommons.org/licenses/by-nc-nd/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited. You may not alter, transform, or build upon this work.
Subject
Engineering not elsewhere classified
Information and Computing Sciences
Technology