# A Multi-Modal Person Recognition System for Social Robots
## 3 Architecture of the Person Recognition System
### Temporal Binding via Spiking Neural Networks
Moreover, IOs that are delivered to independent branches will sum linearly for all of the combinations of IOs intensities. Synaptic integration in dendritic tree of pyramidal neuron was experimentally proved to demonstrate similar behavior to the aforementioned forms of summations [55]. These forms of summations provide a tradeoff between error variance and error bias. Sub-linear summation of within-branch IOs, in case of large IOs, reduces the error variance by not exaggerating the effect of one aspect of the measure at the expense of other measures in deriving the final outcome. In addition, the linearly weighted aggregation of between-branch IOs reduces error bias by means of exploiting various attributes in deriving the final outcome. As shown in Figure 8, the integration of IOs is performed using SNN in time domain to emphasize the temporal binding with fading memory criteria. The IOs represent various scores of confidence; each one of them provides a degree of support for each subject in the gallery set according to a certain aspect of measure and based on a specific biometric modality. These scores are introduced to SNN as presynaptic inputs by means of spikes fired at different times. As described in the previous section, all of the IOs are unified such that high score is equivalent to best match. In order to introduce the IOs to LIF neurons, the IOs are converted to spike times using (11) such that a high IO is equivalent to early firing time. Hence, the neuron which fires first represents the best candidate of the attended subject (from the gallery set). As LIF neurons receive early spikes, which correspond to high degree of support, their membrane potential U increases instantaneously. Once the membrane potential U of one of these neurons crosses the threshold value \(V_{thresh}\), the neuron fires a spike and all neurons participating in the process are reset to \(V_{reset}\). The neuron which fires a spike first, which we refer to as the winner neuron, represents the best candidate of the attended subjects, and the attended subject is labeled with class number assigned to that neuron. The threshold value of the neurons in the SNN controls both the reliability of the perception outcome and the allowed for the perception time of the attended task. A LIF neuron with a high threshold value implies that it will not fire until high intensity presynaptic inputs are delivered to its dendrite branches. These presynaptic inputs may be not available due to the absence of some biometric features or the need for more processing time. Thus, a compromise between the reliability and the reasonable perception time can be achieved by controlling the threshold value, according to a specific scenario of social interaction. As IOs are introduced to LIF neurons in parallel (Figure 8) via presynaptic inputs, one very high IO may drive a neuron to fire a spike and finalize the perception process. This sheds light on the superior feature of this model, such that one biometric feature with high discriminant power may be enough to finalize the perception process. This feature replicates the ability of humans to recognize odd features very quickly [56]. In the face perception and recognition, humans focus on distinctive features, which correspond to very high IOs in this algorithm so that other features may not need to be used. Another vital property of this model is the alleviation of the computational cost in the perception process. As one of the neurons in the final layer fires a spike, all of the neurons that are participating in the perception of attended stimulus are reset and held at that state for a certain time. Early spikes correspond to IOs that carry high discriminant power and consequently provide high degree of support for particular neuron to be the winner neuron and represent the best candidate of attended subject; however, the neuron receiving the earliest spike is not necessarily the winner neuron.