Feature weighted face identification algorithm

A face recognition and feature weighting technology, applied in the face field, can solve problems such as information loss, recognition accuracy needs to be improved, and images are easily ignored.

Inactive Publication Date: 2015-08-26
福建省智慧物联网研究院有限责任公司
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Problems solved by technology

However, using the PCA method, it is easy to ignore other components of the image, and the recognition accuracy still needs to be improved.
Since then, people have also proposed to use classifiers to classify faces, such as support vector machine (SVM) to classify and regress face data. This method is widely used and can process any data, but its accuracy is not the same as The input face feature values ​​have a greater relationship
The face...

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Embodiment Construction

[0046] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0047] As a specific example, such as figure 1 and figure 2 As shown, a new type of examination face authentication system of the present invention includes a data collection device, a data processing device, a cloud storage server and a plurality of entry verification collection devices, and the data collection device is established with the data processing device through a wired or wireless Internet. Data communication connection, the data processing device establishes a data communication connection with the cloud storage server through a wired or wireless local area network,

[0048] The data acquisition device collects character information and image information and generates collected data, which is sent to the data processing device in real time; the data processing device stores the collected data in the corresponding regional classificatio...

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Abstract

The invention relates to a feature weighted face identification algorithm, belongs to the field of human face technology. The feature weighted face identification algorithm comprises the steps that a face image is decomposed into high and low frequency components via wavelet transformation, principal component analysis (PCA) is carried out on the different components to extract feature images, weighing is carried out by an AHP algorithm according to the significance of the components, and a support vector machine (SVM) is used for classification and identification. The image identification rate is highest, and all main information of images is used for identification.

Description

technical field [0001] The invention relates to the field of face technology, in particular to a feature-weighted face recognition algorithm. Background technique [0002] At present, face recognition technology is more and more widely used, such as: face recognition access control system, monitoring system, etc. It has become a hot spot in the field of artificial intelligence and pattern recognition research [1][2]. However, there are still many areas for improvement in face recognition algorithms, such as feature extraction, dimension control, and recognition accuracy. [0003] Due to the relatively high dimensionality of face images, it is common practice to reduce the dimensionality of face images to extract eigenfaces, and then compare them. Among them, principal component analysis (PCA) is to reduce the dimensionality of the image, obtain the principal components of the face image, remove the correlation of the original data, generate the eigenface, and then compare ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06F18/2411
Inventor 庄弘
Owner 福建省智慧物联网研究院有限责任公司
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