Method for identifying human face based on LDA subspace learning

A technology of subspace learning and face recognition, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as ignoring the optimal selection of center points, achieve strong environmental adaptability, enhance effectiveness, and fast calculation speed Effect

Inactive Publication Date: 2011-07-20
苏州市慧视通讯科技有限公司
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Problems solved by technology

In the existing methods, it is assumed that the center point is located at the orig

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  • Method for identifying human face based on LDA subspace learning
  • Method for identifying human face based on LDA subspace learning
  • Method for identifying human face based on LDA subspace learning

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[0017] The specific embodiments of the present invention will be described in further detail below.

[0018] like figure 1 As shown, the steps of the LDA subspace learning method applied to the improved metric post-processing of face recognition according to the present invention are as follows: first, take a photo of a digital camera, a camera, etc., to obtain a face image as a research object, and then sequentially perform the face image Preprocessing, extracting GMLPQ feature set, Adaboost selector, LDA subspace analyzer, and finally performing face feature comparison.

[0019] Attached to the following figure 1 The schematic diagram of the algorithm shown is a detailed description of the specific implementation of the method.

[0020] ①Preprocess the face image, such as normalizing, filtering, and specifying resolution.

[0021] ② Calculate the gradient multi-scale local phase quantization (GMLPQ) feature set of the face image described in ①. GMLPQ feature extraction p...

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Abstract

The invention discloses a method for identifying a human face based on local data area (LDA) subspace learning. The method is characterized by comprising the following steps of: performing pre-processing, namely rotating uprightly a human face image; calculating a gradient multi-scale local phase quantification (GMLPQ) characteristic set of the human face image; screening out a candidate characteristic subset in the GMLPQ characteristic set by using an Adaboost selector; analyzing to acquire a human face characteristic template by using an LDA subspace analyzer; and matching the human face characteristic template with a pre-built human face characteristic template base so as to acquire identity information of a person to be identified. In the method, an optimized central point acquired by learning is used as a central point of a cosine distance, so the effectiveness of metering the sample similarity by calculating a sample distance is improved, and the classification performance of the subspace method is enhanced. The method is high in environmental suitability, and identification rate and error identification rate under vague images, low resolution and various illumination conditions, and quick in calculation speed, particularly suitable for embedded products, and can be popularized and used on a large scale.

Description

technical field [0001] The invention belongs to a face 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 based on 3D model. method. The idea of ​​the method based on geometric features is to extract the rela...

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Application Information

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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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