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Encoding of facial images into illumination-invariant spike trains

Hafiz , Fadhlan and Shafie, Amir Akramin (2012) Encoding of facial images into illumination-invariant spike trains. In: International Conference on Computer and Communication Engineering (ICCCE 2012), 3-5 July 2012, Seri Pacific Hotel Kuala Lumpur.

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Abstract

Some previous work of several researchers have mathematically proven the advantage of Spiking Neural Network (SNN) in term of computational power and one of the neuron model that shows promising result is Spike response Model (SRM). Facial recognition is one of the tasks that can benefit from the advantages of SNN. Therefore in this work we try to unravel the elementary of facial recognition using SNN –the encoding of analog-valued images of the subject face into spike trains as inputs to the neural network using Leaky Integrate and Fire (LIF) model. Implementation of an adaptive LIF model is investigated and a spike adjustment method is proposed to improve the robustness of the generated spikes from a normalized image against different level of illuminations.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 5119/27222
Uncontrolled Keywords: Spiking neurons; integrate-and-fire; image encoding; spike generation; illumination-invariance
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr Amir Shafie
Date Deposited: 06 Dec 2012 14:12
Last Modified: 23 Jan 2013 15:33
URI: http://irep.iium.edu.my/id/eprint/27222

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