Lip print recognition based on convolutional spiking neural network

Abstract

Biological features are widely used for person identification, lip prints have been proved to be a unique and permanent part of the body. Therefore, lip print recognition can be considered as an effective mean to confirm individuals. This paper developed an architecture with convolutional spiking neural network (CSNN) for lip print recognition. Spiking neural networks (SNN) have the potential to reduce power consumption comparing to traditional artificial neural networks (ANN). The leaky-integrate-and-fire (LIF) neuron model and gradient surrogate method were used in the proposed CSNN. This work provided a novel strategy for lip print recognition.