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: | , |
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| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2012
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| 主題: | |
| 在線閱讀: | 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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| 機構: | Universiti Islam Antarabangsa Malaysia |
| 語言: | English |
| 總結: | 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. |
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