Multi-modal face recognition device and method based on multi-layer fusion of gray level and depth information

Abstract

The invention discloses a multi-modal face recognition device and method based on multi-layer fusion of gray level and depth information. The method mainly comprises the steps of recognizing the gray level information of a human face; recognizing the depth information of the human face; normalizing the gray level information and the depth information of the human face, based on normalized matching scores, obtaining multi-modal fused matching scores through fusion strategies to achieve multi-modal face recognition. According to the multi-modal face recognition device and method based on multi-layer fusion of gray level and depth information, a multi-modal system collects two-dimensional gray level information and three-dimensional depth information, makes use of the advantages of the two-dimensional gray level information and the three-dimensional depth information, and through the fusion strategies, overcomes certain inherent shortcomings of a single-modal system such as illumination of gray level images and expressions of depth images, thereby greatly enhancing the performance and achieve accurate and rapid face recognition.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a multi-modal face recognition device and method based on multi-layer fusion of grayscale and depth information. Background technique [0002] Compared with 2D face recognition, 3D face recognition has the advantages of being robust to illumination and less affected by factors such as posture and expression. Therefore, after the rapid development of 3D data acquisition technology and the great improvement in the quality and accuracy of 3D data, Many scholars devote their research to this field. [0003] CN20101025690 proposes the relevant features of three-dimensional bending invariant to describe the characteristics of human face. This method extracts the relevant features of the bending invariant by encoding the local features of the bending invariant of adjacent nodes on the surface of the three-dimensional face; signs the relevant features of the bending invariant a...

AI Technical Summary

Problems solved by technology

[0005] Face grayscale images are easily affected by changes in illumination, while face depth images are easily affected by data acquisition accuracy and expression changes. These factors affect the stability and accuracy of the face recognition system to a certain extent.

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