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Face recognition method and system based on three-dimensional optimization sub-curved surface

A three-dimensional face and face recognition technology, applied in the field of face recognition, can solve the problems of low efficiency and low recognition accuracy, and achieve the effect of reducing the influence of interference factors, calculating quickly, and maintaining the performance of face recognition.

Inactive Publication Date: 2019-12-17
青岛根尖智能科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the deficiencies of the prior art, the present disclosure provides a face recognition method and system based on 3D optimized sub-surfaces, including fast 3D face alignment and 3D sub-surface selection and optimized 3D face recognition, which solves the existing problems Some 3D face recognition methods have low recognition accuracy and low efficiency

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  • Face recognition method and system based on three-dimensional optimization sub-curved surface
  • Face recognition method and system based on three-dimensional optimization sub-curved surface
  • Face recognition method and system based on three-dimensional optimization sub-curved surface

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Embodiment 1

[0056] like Figure 1-4 As shown, Embodiment 1 of the present disclosure provides a face recognition method based on a three-dimensional optimized sub-surface, which specifically includes the following aspects:

[0057] (1) Data collection and preprocessing

[0058] The data acquisition part uses video acquisition equipment including color and depth data to collect face information, and the equipment outputs color images and depth data at the same time, and can correspond pixel by pixel according to depth information. Any device that can collect texture and depth information can be used, including but not limited to laser scanners, structured light sensors, TOF (Time Of Flight, time of flight) cameras, etc.

[0059] This embodiment uses Prime Sense Carmine 1.09, an RGBD sensor based on structured light, which is characterized by an imaging distance of 0.35m-1.4m, a depth accuracy error of 1mm at 0.5m, and an output color image and depth image resolution of 640x480.

[0060] ...

Embodiment 2

[0104] Embodiment 2 of the present disclosure provides a face recognition system based on a three-dimensional optimized sub-surface, including:

[0105] The data collection and preprocessing module is configured to: collect 3D face data, perform smoothing and denoising preprocessing on the 3D face image, and convert the 3D face into a 3D face with a standard posture;

[0106] The data processing module is configured to: sample the three-dimensional face of the standard pose after projection to obtain the current two-dimensional depth map of each face, and perform difference calculation with the pre-stored depth map to obtain two mutually inverse difference maps ;

[0107] The allocator building block is configured to: perform feature selection and optimize classifier training according to the obtained difference map, and obtain feature selection and optimize classifier models based on different sub-surfaces of the difference map;

[0108] The face recognition module is config...

Embodiment 3

[0110] Embodiment 3 of the present disclosure provides a storage medium on which a program is stored. When the program is executed by a processor, the steps of the face recognition method based on the three-dimensional optimized sub-surface described in Embodiment 1 of the present disclosure are implemented.

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Abstract

The invention provides a face recognition method based on a three-dimensional optimization sub-curved surface, and the method comprises the steps: collecting three-dimensional face data, and converting a three-dimensional face into a three-dimensional face with a standard posture; projecting and sampling the three-dimensional face with the standard posture to obtain a two-dimensional depth map ofeach current face, and performing difference calculation on the two-dimensional depth map and a pre-stored depth map to obtain two mutually opposite difference maps; training a feature selection and optimization classifier according to the obtained difference graph to obtain feature selection and optimization classifier models of different sub-curved surfaces based on the difference graph; for each input three-dimensional face, calculating a difference image, calculating a sub-surface feature weighting score according to the calculated difference image and the feature selection and optimization classifier model, finding a pre-stored depth image with the highest score, and obtaining a face recognition result; the influence of the external environment on the system performance is effectivelyweakened, and interference of external factors such as illumination, postures and expressions in face recognition can be effectively avoided.

Description

technical field [0001] The present disclosure relates to the technical field of face recognition, in particular to a face recognition method and system based on a three-dimensional optimized sub-surface, and in particular to a fast three-dimensional face alignment, three-dimensional sub-surface selection and optimization method for three-dimensional face recognition and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Face recognition is a multidisciplinary research topic. Two-dimensional face recognition has a long history of development, has achieved many research results and has been widely used. Since a two-dimensional image is a projection of a three-dimensional object on a plane, the performance of face recognition will be interfered by external factors such as illumination, posture, expression, etc., thereby affecting the...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/40G06K9/62
CPCG06V20/64G06V40/161G06V40/168G06V40/172G06V10/30G06F18/214
Inventor 王跃明王海滨
Owner 青岛根尖智能科技有限公司
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