Facial expression BN recognition method based on small data set

Abstract

The invention relates to a facial expression BN recognition method based on a small data set. Firstly, the geometrical features and HOG features of facial expression images are extracted, a tag sampleset of an action unit (AU) is for by processing such as feature fusion and normalization, Secondly, the BN structure of facial expression recognition is constructed, and the experience of qualitativeexperts is transformed into the constraint set between the conditional probabilities of BN. Then, the objective function is introduced to solve the maximal form of the objective function by convex optimization, and the parameters of BN model are estimated. Finally, the facial expression is recognized by the joint tree inference algorithm. Compared with the prior art, the facial expression recognition method provided by the invention can greatly improve the accuracy of facial expression recognition under the condition of small-scale data set, and can be widely applied to the fields of human-computer interaction, security system, medical health diagnosis, video communication, driver identification and fatigue driving, etc.

Description

technical field [0001] The invention relates to the application field of target recognition of artificial intelligence, image engineering and security systems, and in particular to a facial expression BN recognition method based on a small data set. Background technique [0002] Bayesian network (Bayesian Network, BN) can be expressed as B(G, θ), where G is a directed acyclic graph with n nodes, and n nodes in G represent n random variables. Directed edges represent dependencies between random variables; θ is the conditional probability table associated with each node, denoted as P(X i |P a (X i )). θ quantitatively expresses the node X i with its parent node P a (X i ), the formula (1) is the joint probability distribution of BN: [0003] [0004] Among them, P a (X i ) means X in G i The conditional probability distribution of the parent node set, P(X i |P a (X i )) represents the probability of each value of the variable contained in G given the value of t...

AI Technical Summary

Problems solved by technology

At present, many classic and practical algorithms have been researched and developed in the field of BN's CPT learning, but the implementation and application of these methods are based on large-scale data sets (complete or complete after supplementation), and in practical engineering applications, limited Due to factors such as environment, materials, and time, many experiments cannot be repeated many times, so that the experimental data that can be obtained is small, and the sample size is small. The information that can be expressed in such a small sample data set is not complete enough. The accuracy and reliability of BN parameter learning cannot be guaranteed

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