Design of emotion recognition system using neuromorphic computing technique

Abstract

Human facial expression offers a visual understanding of the underlying human emotions and are comprehensively tapped to automate workflow in the areas of robotics, security, and other assisted and interactive technologies. The paper proposes implementation of human facial emotion recognition (FER) using neuromorphic computing (NC) designed binary synaptic neural network (BSN). An architecture consisting of Viola-Jones (VJ) algorithm with local binary pattern (LBP) as two stage pre-processing steps followed by NC driven synaptic network was designed and investigated for image-based emotion recognition system. The BSN was designed and simulated in system verilog models to showcase an acceptable accuracy of 69.5% for the six targeted emotion classes. NC design showcased comparable classifier accuracy for the emotion recognition system, as that of binary neural network, implemented in the past.