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Face Image Set Recognition Based On Improved HOG-NMF and Convolutional Neural Networks

Lixiu Hao, Weiwei Yu

Abstract

Objective Face recognition can be affected by unfavorable factors such as illumination, posture and expression, but the face image set is a collection of people’s various angles, different illuminations and even different expressions, which can effectively reduce these adverse effects and get higher face recognition rate. In order to make the face image set have higher recognition rate, a new method of combining face image set recognition is proposed, which combines an improved Histogram of Oriented Gradient (HOG) feature and Convolutional Neural Network (CNN). Method The method firstly segments the face images to be identified and performs HOG to extract features of the segmented images. Secondly, calculate the information entropy contained in each block as a weight coefficient of each block to form a new HOG features, and non-negative matrix factorization (NMF) is applied to reduce HOG features. Then the reduced-dimensional HOG features are modeled as image sets which keep your face details as much as possible. Finally, the modeled image sets are classified by using a convolutional neural network. Result The experimental results show that compared with the simple CNN method and the HOG-CNN method, the recognition rate of the method on the CMU PIE face set is increased by about 4%~10%. Conclusion The method proposed in this paper has more details of the face, overcomes the adverse effects, and improves the accuracy.


Keywords

HOG; Non-negative matrix factorization; Convolutional Neural Network; Face image set recognition; Feature extraction

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References

He Chun. A Survey of Face Recognition Technology [J]. Intelligent Computers and Applications, 2016, 6(1): 112-114.

Dong Xiwei, Yao Shimao, Wang Yuwei et al. Multi-manifold discrimination learning algorithm based on virtual sample image set[J]. Application Research of Computers, 2018, 35(06): 1872-1878.

Zhang Wenkai, Sun Hao, Sun Xian, Wang Hongqi. Visual Summary of Image Set Based on MFF-GAN[J]. computer engineering,2019, 45(02): [4] Ren Zhenwen, Wu Mingna. Image set classification algorithm based on entropy self-weighted joint regularization nearest point [J/OL]. computer application: 1-7 [2019-04-14]. http://KNS.cnki.net/kcms/detail/51.1307.TP.20190508.1047.002.html)

Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection[C]. Computer Vision and Pattern Recognition, 2005. Computer Society Conference on IEEE, 2005: 886-893.

XIE Z, JIANG P, ZHANG S. Fusion of LBP and HOG using multiplekernel learning for infrared face recognition[C]. IEEE/ACIS International Conference on Computer and Information Science,2017: 81-84.

BHELE S G, MANKAR V H. Recognition of faces using discriminative features of LBP and HOG descriptor in varying environment[C]. International Conference on Computational Intelligence and Communication Networks, 2016: 426-432.

GHORBANI M, TARGHI A T,DEHSHIBI M M. HOG and LBP:towards a robust face recognition system[C]. Tenth International Conference on Digital Information Management, 2016:138-141.

Sun Rui, Chen Jun, Gao Wei. A Fast Pedestrian Detection Method Based on Significance Detection and HOG-NMF Features[J]. Journal of Electronics & Information Technology, 2013, 35(08): 1921-1926.

Lin Kezheng, Zhang Yuanming, Li Yutian. Research on HOG feature extraction algorithm based on information entropy weighting [J/OL]. computer engineering and applications: 1-10 [2019-05-06]. http://kns.cnki.net/kcms/detail/11.2127.tp.20190411.1719.016.html.

Wang Hai-bo, Chen Yan-xiang, Li Yan-qiu. Face recognition method based on principal component analysis and Softmax regression model[J] Journal of Hefei University of Technology (Natural Science), 2015, 38(06): 759-763.

Zhang Yunming. Research on Face Recognition Algorithm Based on HOG and Gabor Features [D]. Harbin University of Science and Technology, 2019.

Lee H,Yoo J,Choi S. Semi-supervised nonnegative matrix factorization[J]. IEEE Signal Processing Letters, 2010, 17(1): 4-7.

SYAFEEZA A R,KHALIL-HANI M,S S, et al. Convolutional neural network for face recognition with pose and illumination variation[J]. International Journal of Computational Intelligerce & Applica tions, 2015, 14(3): 1-18.

Hou Xiaopei,Gao Ying.Application of Convolutional Neural Network CNN Algorithm in Text Classification[J]. Technology and innovation, 2019(04): 158-159.


DOI: http://dx.doi.org/10.18063/phci.v2i1.1103
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