Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (11): 3201-3206.doi: 10.13229/j.cnki.jdxbgxb.20220813

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Adaptive blur and deduplication algorithm for digital media image based on wavelet domain

Xiao-qi LYU1,2,3(),Hao LI1,2,Yu GU1,2   

  1. 1.College of Information Engineering，Inner Mongolia University of Science and Technology，Baotou 014010，China
    2.Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Imag Processing，Inner Mongolia University of Science and Technology，Baotou 014010，China
    3.Institute of Information Engineering，Inner Mongolia University of Technology，Hohhot 010051，China
  • Received:2022-06-27 Online:2023-11-01 Published:2023-12-06

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Abstract:

The quality of face feature extraction results affects the accuracy of face recognition. At present， the feature extraction methods of face images still have the problems of low extraction accuracy and low efficiency. In order to solve the problems in the methods， a scale extraction method of face image living feature transformation based on deep learning algorithm is proposed. The deep learning method is used to denoise the face image. Based on this， Gabor wave filter is used to decompose the face signal and input it into the deep subspace model to extract the feature transform scale. Based on PSO （particle swarm optimization）， the scale extraction of face image living feature transformation is completed. The experimental results show that the proposed face image feature extraction method has higher accuracy， faster recognition speed and better overall application effect.

Key words: facial feature extraction, deep learning, denoising, Gabor filter, deep subspace model

CLC Number: 

  • TP391.41

Fig.1

Experimental Image Set"

Fig.2

Image noise robustness of algorithm in reference ［3］"

Fig.3

Image noise robustness of algorithm in reference ［4］"

Fig.4

Image noise robustness of the proposed algorithm"

Table 1

Average recognition rate of ORL face database"

训练样本ORL人脸数据库平均识别率/%
本文方法文献［3］方法文献［4］方法
182.680.280.6
292.881.992.3
393.290.391.6
494.692.891.8
592.691.790.3
694.592.891.5
793.692.491.7
892.591.490.9
990.489.388.9
1093.392.191.6

Fig.5

Face image recognition time"

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