Face recognition method based on combination of partial principal component analysis (PCA) and attitude estimation

A principal component analysis and attitude estimation technology, applied in the field of virtual three-dimensional face map, can solve the problems of inability to obtain three-dimensional information, large amount of calculation, inappropriate real-time processing, etc., to reduce complexity, high robustness, and beneficial to Effects of real-time processing

Inactive Publication Date: 2011-05-04
ZHEJIANG UNIV
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Problems solved by technology

However, there are some problems with this method: first, the amount of calculation is large, and it is not suitable for real-time processing; second, a large number of 3D scanning models are required to build a database. In pra

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  • Face recognition method based on combination of partial principal component analysis (PCA) and attitude estimation
  • Face recognition method based on combination of partial principal component analysis (PCA) and attitude estimation
  • Face recognition method based on combination of partial principal component analysis (PCA) and attitude estimation

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[0029] Below, in conjunction with the accompanying drawings and specific embodiments, the present invention will be further illustrated by taking the UPC face database as an example. The UPC face database is composed of 44 face images, and each person has 27 face images in 3 different lighting conditions (natural light, strong light in the direction of 45°, and strong light in the direction of 0°). 9 different attitudes (0°, ±30°, ±45°, ±60°, ±90°). The left face direction is assumed to be the positive direction. In this embodiment, only attitude changes under natural light are considered, and other lighting changes are not considered.

[0030] One, such as figure 1 As shown, the steps of this embodiment are as follows:

[0031] Step 1: Store the virtual three-dimensional face map in advance in the original sample library, and apply the principal component analysis method to calculate the extended face space in the original sample library:

[0032] The two-dimensional face...

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Abstract

The invention discloses a face recognition method based on the combination of partial principal component analysis (PCA) and attitude estimation, comprising the following steps of: (1) previously storing a virtual three-dimensional face image in an original sample library, using a PCA method to compute a face-expanding space in the original sample library, and using a partial PCA method to combine with an eyes-mouth automatic positioning algorithm to perform attitude estimation to two two-dimensional training face images in different attitudes; (2) searching corresponding sub-face space in the face-expanding space according to the attitude estimation result; (3) generating a new virtual three-dimensional face image according to two two-dimensional training face images in different attitudes, the face-expanding space and the sub-face space; (4) using the new three-dimensional face image to update the original sample library; (5) using the partial PCA method to combine with the eyes-mouth automatic positioning algorithm to perform the attitude estimation to the to-be-recognized two-dimensional face image; (6) using the partial PCA method to recognize the to-be-recognized two-dimensional face image.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and relates to a pose robust face recognition method based on the combination of partial principal component analysis and pose estimation, which can generate virtual three-dimensional face images according to two-dimensional face images of different poses. Background technique [0002] In recent decades, face recognition has been a research hotspot in the field of computer vision. It has a wide range of practical applications and can be applied to the field of human-computer interaction and video surveillance. Changes in illumination, expression, and posture increase the difficulty of face recognition. Among them, posture changes are the biggest bottleneck of face recognition and are still a challenging subject. In order to solve this problem, many methods have been proposed, which can be divided into three aspects: based on single-view, based on multi-view, and based on 3D models. In t...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 潘翔王玲玲郭小虎
Owner ZHEJIANG UNIV
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