Three-dimensional face recognition method based on Bayesian multivariate distribution characteristic extraction

Abstract

The invention discloses a three-dimensional face recognition method based on Bayesian multivariate distribution characteristic extraction. The method comprises three-dimensional data preprocessing, characteristic extraction and identification classification. The method has the advantages that the defect of large computation amount in the prior art is overcome; a three-dimensional face depth map is used for identifying, so that the computation amount can be reduced, and the identification efficiency is increased; the problem of insufficient training samples in single sample identification can be solved, and the training samples are added with a partitioning method; a characteristic extraction method based on Bayesian analysis is provided on the basis, so that the obtained characteristics have a minimum intra-class distance and a maximum inter-class distance, namely, optimal separability is realized; moreover, a classification method based on Mahalanobis distance is adopted, so that optimal identification classification is realized. As proved by experiment data, the method disclosed by the invention has a good three-dimensional face recognition result.

Description

technical field [0001] The invention belongs to the technical field of biological feature recognition, and in particular relates to a new three-dimensional face recognition method based on Bayesian multivariate distribution feature extraction. Background technique [0002] Face recognition has gained enormous attention in the past few decades in both science and industry. Among them, two-dimensional face recognition technology has been widely studied. However, under the constraints of lighting conditions, poses, and representations, 2D face recognition remains a challenge. In this regard, the three-dimensional shape data of the face can be regarded as not changing with the change of illumination and view, and the accessories such as make-up have a great influence on the image but have no obvious influence on the three-dimensional data. Therefore, 3D face recognition is considered to have the characteristics of invariant illumination and pose invariance, which is more benef...

AI Technical Summary

Problems solved by technology

[0005] One of the main difficulties in 3D face recognition is that it is difficult to collect 3D face samples

Under the condition of a single training sample, due to the lack of training samples in the high-dimensional space, the recognition effect of the above method is not ideal

Therefore, for the single-sample recognition problem, the main challenge is the feature extraction of 3D faces.

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