Face complex expression recognition method based on neural network

Abstract

The invention discloses a face complex expression recognition method based on a neural network. On the basis of a general back-propagation neural network, a face recognition technology combining a method based on face feature sub-graph extraction and a method based on biometric parameter extraction is employed. In the method based on face feature sub-graph extraction, four sub-images of the eyes, nose and mouth are extracted and fed to one general back-propagation neural network. In the method based on biometric parameter extraction, seven measured distances between the facial feature points are fed to the other general back-propagation neural network. The network used in the method based on face feature sub-graph extraction is selected as a main neural network, while the network used in the method based on biometric parameter extraction serves as an auxiliary neural network. If a main classifier fails to recognize a complex expression, an auxiliary classifier is used instead.

Description

Technical field: [0001] The invention relates to the field of biometric feature recognition, in particular to a neural network-based recognition method for facial complex expressions. Background technique: [0002] In the highly informationized social environment, traditional identity authentication methods have many shortcomings such as not easy to carry, easy to forge, easy to damage, vulnerable to attack, etc., and it is difficult to meet the needs of the development of an intelligent society. In recent years, the uniqueness, stability, security and universality of biometric identification have made it an extremely important emerging technology in the current information security field. Among them, the naturalness, non-contact, concealment and high reliability of face recognition make it have greater application prospects than other recognition methods such as fingerprints, palmprints, iris, and voice. [0003] Facial expression is an important way of human information e...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, utilize the stability of the distance between the local features of the face and the feature points when the expression changes, and apply the combination of extraction based on biological feature parameters and extraction based on facial feature submaps. The facial component feature detector of the two methods, and then use the general back-propagation neural network to realize the classification recognition method based on the neural network complex facial expression recognition

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