Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning
We propose a reward based learning to associate face and voice stimuli. In particular, we implement learning in a spiking neural network paradigm using modulated spike-time dependent plasticity (STDP).The face and voice stimuli are paired with a temporal delay, and the network is trained to associ...
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my.uum.repo.155182016-04-28T01:58:10Z http://repo.uum.edu.my/15518/ Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning Yusoff, Nooraini Ibrahim, Mohammed Fadhil QA75 Electronic computers. Computer science We propose a reward based learning to associate face and voice stimuli. In particular, we implement learning in a spiking neural network paradigm using modulated spike-time dependent plasticity (STDP).The face and voice stimuli are paired with a temporal delay, and the network is trained to associate the paired face-voice with a target response.The learning rule is dependent on a reward policy in which the network is given a positive reward for a correct response to a face-voice stimulus pair, or the network receives a negative reward for an incorrect response. Despite a stochastic environment, the learning result of real images and sound indicates a good performance with 77.33% accuracy.The result demonstrates that a machine can be trained to associate a pair of biometric inputs to a target response. 2015-08-11 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/15518/1/PID161.pdf Yusoff, Nooraini and Ibrahim, Mohammed Fadhil (2015) Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey. http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
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QA75 Electronic computers. Computer science Yusoff, Nooraini Ibrahim, Mohammed Fadhil Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning |
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We propose a reward based learning to associate face and voice stimuli. In particular, we implement learning in a spiking neural network
paradigm using modulated spike-time dependent plasticity (STDP).The face and voice stimuli are paired with a temporal delay, and the network
is trained to associate the paired face-voice with a target response.The learning rule is dependent on a reward policy in which the network is given
a positive reward for a correct response to a face-voice stimulus pair, or the network receives a negative reward for an incorrect response. Despite a stochastic environment, the learning result of real images and sound indicates a
good performance with 77.33% accuracy.The result demonstrates that a machine can be trained to associate a pair of biometric inputs to a target response. |
format |
Conference or Workshop Item |
author |
Yusoff, Nooraini Ibrahim, Mohammed Fadhil |
author_facet |
Yusoff, Nooraini Ibrahim, Mohammed Fadhil |
author_sort |
Yusoff, Nooraini |
title |
Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning |
title_short |
Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning |
title_full |
Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning |
title_fullStr |
Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning |
title_full_unstemmed |
Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning |
title_sort |
face-voice association towards multimodal-based authentication using modulated spike-time dependent learning |
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2015 |
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http://repo.uum.edu.my/15518/1/PID161.pdf http://repo.uum.edu.my/15518/ http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
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13.18916 |