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A fecnn-based face feature extraction system and method

A feature extraction and face feature technology, applied in the field of face recognition, can solve the problems of large number of iterations, inability to fully represent the face, long convergence time, etc., achieve robust face depth features, improve extraction speed and accuracy sexual effect

Active Publication Date: 2020-04-24
GUILIN UNIV OF ELECTRONIC TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a kind of human face feature extraction system based on FECNN, the technical problem to be solved is: the feature that shallow CNN network extracts can't represent human face fully, and the CNN network of deep layer is faced with too many iterations , the convergence time is too long

Method used

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  • A fecnn-based face feature extraction system and method
  • A fecnn-based face feature extraction system and method
  • A fecnn-based face feature extraction system and method

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Experimental program
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Embodiment 1

[0045] like Figure 1 to Figure 3 As shown, a face feature extraction system based on FECNN, including face preprocessing module 1, FECNN (FECNN is Fast and Effective Convolutional Neural Networks, is an efficient convolutional neural network) module 2, feature extraction module 3 and feature ratio for module 4;

[0046] Described human face preprocessing module 1 is used for carrying out human face detection to human face pictures, and the detected human face image is cut out, and the human face key point is located on the human face image, and then the human face key point is combined with Align face images;

[0047] The FECNN module 2 is used to build the FECNN framework for feature extraction, and uses the training library sample face to train the FECNN framework until the FECNN framework converges to obtain the FECNN parameter model;

[0048] The feature extraction module 3 is used to extract the FECNN to extract the network parameter model, send the face key points and...

Embodiment 2

[0066] like Figure 4 Shown, a face feature extraction method based on FECNN, including the following steps:

[0067] Step S1. Face preprocessing module 1 performs face detection on the face picture, and cuts the detected face image, locates the key points of the face on the face image, and then compares the key points of the face with the face The images are aligned; the FECNN module 2 builds the FECNN framework for feature extraction, and uses the training library sample face to train the FECNN framework until the FECNN framework converges to obtain the FECNN parameter model;

[0068] Step S2. The feature extraction module 3 extracts the FECNN to extract the network parameter model, sends the key points of the face and the face image into the FECNN parameter model for feature extraction, and outputs the face features;

[0069] Step S3. The feature comparison module 4 uses the cosine distance to calculate the face features. When the calculated distance is greater than the se...

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Abstract

The present invention relates to a system and method for extracting human face features based on FECNN. The system includes a human face preprocessing module to detect human face images, and to cut, locate and align detected human face images; the FECNN module Build the FECNN framework for feature extraction, and train until the FECNN framework converges to obtain the FECNN parameter model; the feature extraction module sends the key points and face images of the face to the FECNN parameter model to extract face features; the feature comparison module uses cosine The distance is calculated on the facial features. When the distance is greater than the set threshold t, it is judged as the same person, and when the distance is smaller than the set threshold t, it is judged as different people. Compared with the prior art, the present invention uses fewer parameters to quickly converge the network model and extract robust face features.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a system and method for extracting face features based on FECNN. Background technique [0002] Face recognition is currently one of the hottest research topics in the fields of machine learning, pattern recognition and computer vision, and it has more and more applications in practical engineering. After decades of research, fruitful results have been obtained. In recent years, CNN has been used for image processing many times, and it has been proved that it is more practical and has higher accuracy than traditional methods in feature extraction of targets. In particular, CNN has made breakthrough progress in face recognition, showing unlimited prospects; but in the existing technology, the features extracted by the shallow CNN network cannot fully represent the face, and the deep CNN network Faced with too many iterations and too long convergence time. Contents of t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/165G06V40/172G06V40/171G06N3/045
Inventor 蔡晓东吕璐
Owner GUILIN UNIV OF ELECTRONIC TECH
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