A Heterogeneous Spiking Neural Network for Computationally Efficient Face Recognition

Abstract

Computational efficiency is critical to many mobile and always-on face recognition applications. To this end, a heterogeneous spiking neural network (SNN) is proposed for face recognition. To obtain high recognition accuracy at minimal computational overheads, the heterogeneous SNN consists of an encoding subnet for sparse image feature encoding and classification subnet for feature classification. The experimental results suggest that the proposed heterogeneous algorithm can achieve high recognition accuracy on small datasets of human face samples with labeled identities at a high computational efficiency with very low neuronal activities. The proposed SNN is promising for low-cost mobile or always-on systems with strictly constrained resource and energy budgets.