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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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | 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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| Summary: | 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. |
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