Multi-view angle human face recognizing method based on non-linear tensor resolution and view angle manifold

Abstract

The invention discloses a method for identifying multi-view human face based on nonlinear tensor resolution and view manifold, which comprises the following steps: normalizing the size of the multi-view human face; dividing multi-view human face images into a test set and a training set by adopting a method of leaving one out; arraying the human face images in the training set into a form of tensor along the direction of identity, view and pixel information change, resolving tensor data by using high-order singular values to obtain a coefficient matrix of identity, view and pixel factors of the human face images; using a data-concept driving mode to array and interpolate view coefficients to obtain the view manifold of human face; according to the rotating objective sequence of the human face, generating the view manifold through a concept driving mode; using the nonlinear tensor resolution to map the view manifold to a data space of the multi-view human face, obtaining a modular matrix of identity coefficient, and establishing a model of the multi-view human face; and adopting an iterative algorithm based on EM-like to solve a model parameter, and achieving identification by the parameter meeting the minimum reconstructed error criterion. The method has the advantages of high accuracy and high speed, and can be used for complex human face retrieval and identification under different view angles in the field of biological characteristic identification.

Description

Multi-view face recognition method based on nonlinear tensor decomposition and view manifold Technical field The invention belongs to the technical field of pattern recognition and computer vision, and particularly relates to a multi-view face recognition method, which can be used for face retrieval and recognition under different viewing angles in the field of biometric recognition. Background technique Biometric identification will become one of the most important technological revolutions in the security and IT industry in the next few years. Modern research shows that human iris, fingerprints and palm shape can be used for identification. Compared with other biometric recognition technologies or systems, face recognition systems have the advantages of high recognition accuracy, convenience, friendliness, and naturalness. Therefore, face recognition is a non-invasive identification method that is easily accepted by people. Face imaging is affected by the interaction of va...

AI Technical Summary

Problems solved by technology

However, for complex face images with multi-factor changes, this method cannot establish a unified face surface model, so it cannot separate various factors that affect face generation, nor can it accurately describe the linear and nonlinear aspects of face space. linear change

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