Efficient Eye-based Emotion Recognition via Neural Architecture Search of Time-to-First-Spike-Coded Spiking Neural Networks

Eye-based emotion recognition enables eyewear devices to perceive users' emotional states and support emotion-aware interaction, yet deploying such functionality on their resource-limited embedded hardware remains challenging. Time-to-first-spike (TTFS)-coded spiking neural networks (SNNs) offer a promising solution, as each neuron emits at most one binary spike, resulting in extremely sparse and energy-efficient computation. While prior works have primarily focused on improving TTFS SNN training algorithms, the impact of network architecture has been largely overlooked. In this paper, we propose TNAS-ER, the first neural architecture search (NAS) framework tailored to TTFS SNNs for eye-based emotion recognition. TNAS-ER presents a novel ANN-assisted search strategy that leverages a ReLU-based ANN counterpart sharing an identity mapping with the TTFS SNN to guide architecture optimization. TNAS-ER employs an evolutionary algorithm, with weighted and unweighted average recall jointly defined as fitness objectives for emotion recognition. Extensive experiments demonstrate that TNAS-ER achieves high recognition performance with significantly improved efficiency. Furthermore, when deployed on neuromorphic hardware, TNAS-ER attains a low latency of 48 ms and an energy consumption of 0.05 J, confirming its superior energy efficiency and strong potential for practical applications.