Partially Occluded Ear Recognition Based on Gabor Wavelet Transform PCA and PSO-BP Neural Network

Partially Occluded Ear Recognition Based on Gabor Wavelet Transform PCA and PSO-BP Neural Network

Abstract: On the basis of the human ear Gabor features, PCA dimensionality reduction and the artificial neural network based on PSO training are used to identify partially occluded human ear. The ear image PCA dimensionality reduction of Gabor feature value is selected as the training samples of neural network. The network is trained to take the particle swarm optimization algorithm combined with BP algorithm. Comparative experiments with other methods indicate that the partially occluded ear recognition based on the Gabor wavelet transform + principle component analysis and PSO-BP neural network method has higher recognition performance and can achieve good results.#br# Key words: ear recognition; Gabor feature; principle component analysis; particle swarm optimization

Online:2014-02-28 Published:2015-03-26

Abstract

Abstract: On the basis of the human ear Gabor features, PCA dimensionality reduction and the artificial neural network based on PSO training are used to identify partially occluded human ear. The ear image PCA dimensionality reduction of Gabor feature value is selected as the training samples of neural network. The network is trained to take the particle swarm optimization algorithm combined with BP algorithm. Comparative experiments with other methods indicate that the partially occluded ear recognition based on the Gabor wavelet transform + principle component analysis and PSO-BP neural network method has higher recognition performance and can achieve good results.

Key words: ear recognition, Gabor feature, principle component analysis, particle swarm optimization

Gao Yanxia. Partially Occluded Ear Recognition Based on Gabor Wavelet Transform PCA and PSO-BP Neural Network[J]. Journal of Graphics.

[1]	LIU Si-meng, WANG Gang-feng, SUO Xue-feng. Optimization of milling parameters for thin-walled parts based on improved chaotic particle swarm optimization algorithm [J]. Journal of Graphics, 2021, 42(6): 987-994.
[2]	MA Tian, YANG Qin, LI Zhan-i. Orthodontic path planning based on improved multi-PSO [J]. Journal of Graphics, 2021, 42(4): 615-622.
[3]	YANG Yan-pu. Kansei evaluation method of product form design based on hesitant fuzzy linguistic term sets and particle swarm optimization [J]. Journal of Graphics, 2021, 42(4): 680-687.
[4]	LI Hao, WANG Hongtao, DONG Qingqing. Automated Pipe Defect Detection and Classification [J]. Journal of Graphics, 2017, 38(6): 851-856.
[5]	Wang Sen, Wang Lu, Hong Jinghui, Li Sihui, Sun Xiaopeng. 3D Ear Point Clouds Recognition Using Sparse ICP [J]. Journal of Graphics, 2015, 36(6): 862-867.
[6]	Zhang Dejun, He Fazhi, Li Xiaoxia. Spline Sketch Exchange between Heterogeneous CAD Systems [J]. Journal of Graphics, 2015, 36(2): 215-221.
[7]	Miao Jingjing, Cui Changcai, Zhou Lijun, Yu Qing, Ye Ruifang. Grains Detection of Diamond Grinding Wheel Based on Particle Swarm Optimization [J]. Journal of Graphics, 2015, 36(2): 238-243.
[8]	Li Haiyang, Wen Yongge, He Hongzhou, Li Bolin. An Image Segmentation Algorithm Based on Random Weight Particle Swarm Optimization and K-means Clustering [J]. Journal of Graphics, 2014, 35(5): 755-761.

Partially Occluded Ear Recognition Based on Gabor Wavelet Transform PCA and PSO-BP Neural Network

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 8

Recommended Articles

Metrics

Comments