Dynamic expression recognition method combined with biomorphic neuron model

Abstract

The invention provides a dynamic expression recognition method combined with a biomorphic neuron model. A dynamic face image is selected in a certain time interval; an original pixel is converted intoa pulse sequence by adopting a frequency coding method, then a neuron model which is multiplied, accumulated and then non-linearly activated is replaced with an LIF neuron model which is closer to real biological characteristics, and expression recognition of a dynamic face is carried out in combination with a convolutional neural network structure. The capability that the artificial neural network CNN is good at processing spatial information is fully utilized; the capability that a pulse network structure based on an LIF neuron model is good at processing time sequence information is combined; according to the method, a hybrid network model is formed by fusing the two images, the problem of dynamic facial expression recognition is solved, and compared with an artificial neural network CNN method of a single face image, the hybrid network model has higher recognition accuracy by utilizing dynamic space-time characteristics; due to the adoption of the event-driven spiking neuron model, the parameter calculation amount is lower, and the power consumption is lower.

Description

technical field [0001] The invention relates to the fields of neurology and computing science, in particular to a dynamic expression recognition method combined with a biomorphic neuron model. Background technique [0002] Face recognition technology is based on human facial features. For the input face image or video stream, it is first judged whether there is a human face. If there is a human face, the position, size and each main The location information of facial organs, and based on this information, further extract the identity features contained in each face, and compare them with known faces to identify the identity of each face. Face recognition in a broad sense actually includes a series of related technologies for building a face recognition system, including face image acquisition, face positioning, face recognition preprocessing, identity confirmation, and identity search; A technology or system for face recognition or identity search. [0003] Due to the char...

AI Technical Summary

Problems solved by technology

However, in the industry, facial expression recognition has always been a difficult problem, limited by the variety of facial expressions in a short period of time. It is difficult to describe the real facial expression changes of the current person if only one frame of face image is used for facial expression recognition. Recognition is limited by the consumption of computing power, and different results given by different frames, and it is difficult to evaluate intuitively

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