Feature Weighted Face Recognition Algorithm

A face recognition and feature weighting technology, applied in the face field, can solve problems such as easy to ignore images, information loss, and recognition accuracy to be improved

Inactive Publication Date: 2018-06-05
福建省智慧物联网研究院有限责任公司
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AI Technical Summary

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 recognition method based on wavelet transform, through the multi-scale decomposition of the image, can decompose the face into different parts with high and low frequencies. Usually, the low-frequency part with rich image information is used for face recognition. The method has achieved certain results and improved. Improve the recognition accuracy, but remove the high-frequency part of the image, resulting in the loss of part of the information

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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 present invention relates to the technical field of human face, in particular to a feature-weighted human face recognition algorithm, comprising the following steps: using wavelet transform to decompose a human face image into high and low frequency components, and performing principal component analysis (PCA) on different components to extract feature images According to the importance of each component, AHP algorithm is used for weighting, and support vector machine (SVM) is used for classification and recognition. The invention has the highest image recognition rate, and all the main information of the image is used for recognition.

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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Patent Type & Authority Patents(China)
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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