Multi-modal face recognition method performing through face depth prediction

Abstract

The invention discloses a multi-modal face recognition method performing through face depth prediction. The method comprises the steps of data extracting, wherein the face segmentation and scaling ofDepth modal data are conducted, cascade network model training, face recognition network training, model merging and testing phase performing. According to the method, not only is the face Depth imagepredicted by a network model close to a real face Depth image, but also the between-class distance of the predicted Depth image is enhanced by a cascaded classification network, so that the predictedface Depth image has richer identification information. According to the method, a face Depth modal is predicted through a face RGB modal to increase the diversification of face modal data, and multi-modal face recognition can be achieved under the premise that the existing hardware of an RGB camera is not changed. By combining the RGB modal and the predicted Depth modal, the accuracy of the multi-modal face recognition is higher than the accuracy of the face recognition only using RGB modal data.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a multi-modal face recognition method through face depth prediction. Background technique [0002] RGB (red, green, blue three-channel color) single-mode face recognition technology has reached the bottleneck, but under uncontrollable conditions, such as changes in facial posture, especially changes in illumination, the RGB face Recognition accuracy still has a big impact. With the emergence and popularization of Kinect and RealSense cameras, people can obtain face data of more modes other than RGB, such as Depth (depth) and NIR (near infrared). Face data in Depth and NIR modes are not affected by light, and accurate face information can be obtained even in the dark. Therefore, combining the two modalities of RGB and Depth for multimodal face recognition can greatly improve the robustness to illumination. However, the currently widely used cameras are still ordinary RGB cameras. If...

AI Technical Summary

Problems solved by technology

This technical method has the following defects: it uses various modal information of the face, but these information are collected from the corresponding equipment, and the hardware equipment requirements are relatively high in the actual face recognition application; and in this technology, except RGB The face positioning of other modalities except the RGB mode is obtained from the face positioning mapping of the RGB mode, so that when the face positioning of the RGB modal data is inaccurate or the face positioning cannot be performed (it cannot be detected in a dark environment Face positioning in RGB mode), the face recognition effect of other modes is limited by the face recognition effect of RGB mode

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