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Encoding of facial images into illumination-invariant spike trains

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 t...

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Main Authors: Hafiz , Fadhlan, Shafie, Amir Akramin
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/27222/1/06271167.pdf
http://irep.iium.edu.my/27222/
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6271167
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spelling my.iium.irep.272222013-01-23T07:33:52Z http://irep.iium.edu.my/27222/ Encoding of facial images into illumination-invariant spike trains Hafiz , Fadhlan Shafie, Amir Akramin TK7885 Computer engineering 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. 2012-07-03 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/27222/1/06271167.pdf 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. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6271167
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Hafiz , Fadhlan
Shafie, Amir Akramin
Encoding of facial images into illumination-invariant spike trains
description 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.
format Conference or Workshop Item
author Hafiz , Fadhlan
Shafie, Amir Akramin
author_facet Hafiz , Fadhlan
Shafie, Amir Akramin
author_sort Hafiz , Fadhlan
title Encoding of facial images into illumination-invariant spike trains
title_short Encoding of facial images into illumination-invariant spike trains
title_full Encoding of facial images into illumination-invariant spike trains
title_fullStr Encoding of facial images into illumination-invariant spike trains
title_full_unstemmed Encoding of facial images into illumination-invariant spike trains
title_sort encoding of facial images into illumination-invariant spike trains
publishDate 2012
url http://irep.iium.edu.my/27222/1/06271167.pdf
http://irep.iium.edu.my/27222/
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6271167
_version_ 1643609296238280704
score 13.211869

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