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Face recognition method based on LDA (Linear Discriminant Analysis) subspace learning

A subspace learning, face recognition technology, applied in character and pattern recognition, instruments, computer parts and other directions, can solve the problem of low stability

Inactive Publication Date: 2011-08-03
苏州市慧视通讯科技有限公司
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Problems solved by technology

The idea of ​​the method based on geometric features is to extract the relative position and relative size of the representative parts of the face (such as eyebrows, eyes, nose, mouth, etc.) Easily affected by factors such as light, expression, occlusion, etc., the stability is not high

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  • Face recognition method based on LDA (Linear Discriminant Analysis) subspace learning
  • Face recognition method based on LDA (Linear Discriminant Analysis) subspace learning
  • Face recognition method based on LDA (Linear Discriminant Analysis) subspace learning

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Embodiment Construction

[0018] Specific embodiments of the present invention will be further described in detail below.

[0019] Such as figure 1 Shown, the LDA subspace learning method step of the improved objective function that is applied to face recognition of the present invention is: face image acquisition, extraction GMLPQ feature set, Adaboost selector, LDA subspace analyzer, carry out face feature at last Comparison.

[0020] The following is attached figure 1 The schematic diagram of the algorithm is shown, and the specific implementation of the method is described in detail.

[0021] ① Obtain the face image, and perform preprocessing such as normalization, filtering, and specified resolution.

[0022] ② Calculate the gradient multi-scale local phase quantization (GMLPQ) feature set of the face image described in ①.

[0023] GMLPQ feature extraction principle:

[0024] The GMLPQ feature is to extract the MLPQ feature based on the gradient image, and the gradient image includes a horizo...

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Abstract

The invention discloses a face recognition method based on LDA (Linear Discriminant Analysis) subspace learning, which is characterized in that the method comprises the following steps: obtaining and preprocessing a face image; calculating a gradient multi-scale local phase quantification (GMLPQ) feature set of the face image; screening features with identification capability to form a candidate feature subset by applying an Adaboost selector; obtaining a face feature template by applying an LDA subspace analyzer; matching the face feature template with a reestablished face feature template library and obtaining the identity information of a recognized person. Compared with the existing other face recognition technologies, the face recognition technology of the face recognition method has stronger environmental suitability, better recognition rate and misclassification rate under the conditions of blurry images (defocus, movement and the like), low resolution and various illumination (infrared light and visible light), and quick calculation speed, is particularly suitable for embedded products and can be extensively popularized and applied.

Description

technical field [0001] The invention belongs to the face recognition technology, in particular to the face recognition technology based on the LDA subspace recognition method. Background technique [0002] Face recognition technology is one of the biometric technologies that are currently being vigorously developed. The face recognition system mainly includes data acquisition subsystem, face detection subsystem and face recognition subsystem. Face feature extraction is the most critical technology of the face recognition subsystem. A good face feature extraction technology will make the extracted face feature value smaller and better in discrimination performance, which can improve the recognition rate and reduce the false recognition rate. The existing face feature extraction methods mainly include: based on geometric features, based on subspace analysis, based on wavelet theory, based on neural network, based on hidden Markov model, based on support vector machine and bas...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/60G06K9/62
Inventor 刘文金赵春水刘宝
Owner 苏州市慧视通讯科技有限公司
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