Face image virtual sample generating method

Abstract

The invention discloses a face image virtual sample generating method. The facial image virtual sample generating method comprises the steps of conducting control point calibration on a source posture face image training sample and a target posture face image training sample, then using a source posture face image training sample set and a target posture face image training sample set as input and output of an RBF neural network, and obtaining an RBF neural network fitting model through training; generating a source coordinate matrix according to source posture face images, inputting the source coordinate matrix into the RBF neural network fitting model to obtain a coordinate transformation matrix, conducting textural feature mapping according to the source coordinate matrix and the coordinate transformation matrix, then conducting interpolation on textural feature deficiency points to obtain a target posture face image virtual sample and finally conducting normalization operation and saving on the target posture face image virtual sample. The face image virtual sample generating method adopts the RBF neural network fitting model and enables the generated face image virtual sample to be approximate to a real sample, and further face recognition rate is further improved.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and more specifically relates to a method for generating a virtual sample of a face image. Background technique [0002] Face recognition technology is one of the hot research issues in the field of computer vision and artificial intelligence. The advantages of the face as a biological feature in the field of identification are: (1) The cooperation requirements of the person to be identified are relatively low. In many cases, the target to be identified rarely or even does not need to cooperate actively, so the identification process is relatively hidden. ; (2) Compared with biological characteristics such as iris and fingerprints, the feature collection of the face does not require special equipment, which makes the recognition cost lower and more convenient; (3) The current face database is relatively rich, except for professional databases. , public security and other departments hav...

AI Technical Summary

Problems solved by technology

However, the current face recognition has the following difficulties: 1. Face recognition based on small samples: Since the collection of face training data requires the cooperation of the personnel to be recognized, when the cooperation degree of personnel is low, often There are only a small amount of single-view or single-sample face data; 2. Multi-pose face recognition problems: classic face recognition methods such as PCA eigenfaces, Fisherface and other methods are sensitive to pose changes. When the face pose changes, the recognition rate drops rapidly

[0006] Among them, polynomial transformation is one of the best methods in this kind of method. The current method is the bivariate quadratic polynomial fitting method proposed by Zhu Changren in 2001. There are still some errors in the face

Moreover, the face image space is an ultra-high-dimensional space. The deformation of this ultra-high-dimensional space will be a nonlinear deformation, and polynomial fitting needs a higher number of times to gradually approach this transformation. However, due to the current As the number of polynomial fitting methods increases, the number of calibration control points required by the algorithm increases exponentially, while polynomial fitting has no ability to suppress errors, so as the number of control points increases, the fitting error also increases significantly , so that in the actual situation, the fitting ability of high-order polynomial fitting does not increase with the increase of the number of fittings

Therefore, the method of generating virtual faces by fitting posture changes with high-degree polynomials has certain limitations.

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