doi: 10.3934/dcdss.2019060

An efficient face recognition algorithm using the improved convolutional neural network

Qingdao Vocational and Technical College of Hotel Management, Qingdao 266100, China

* Corresponding author: Honggang Yu

Received  August 2017 Revised  December 2017 Published  November 2018

This paper concentrates on the problem of human face recognition problem, which is a crucial problem in computer vision. In this paper, the semi-supervised learning based convolutional neural network is used to implement the face recognition system with high efficiency. Convolutional neural networks denote a multi-layer neural network, in which each layer is made up of multiple two-dimension planes and each plane consists of a lot of independent neurons. To extract the rich and discriminative information of human face images, the sparse Laplacian filter learning is utilized to learn the filters of the network with a large scale unlabeled human face images. Afterwards, a softmax classifier layer is trained by multi-task learning using only a small number of labeled human face images as the output layer. In the end, a series of experiments are conducted to test the performance of our proposed algorithm. Experimental results show that face recognition accuracy of the proposed improved CNN method performs better than other methods.

Citation: Honggang Yu. An efficient face recognition algorithm using the improved convolutional neural network. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2019060
References:
[1]

O. Abdel-HamidA.-R. MohamedH. JiangL. DengG. Penn and D. Yu, Convolutional neural networks for speech recognition, IEEE-ACM Transactions on Audio Speech and Language Processing, 22 (2014), 1533-1545.

[2]

R. T. G. AlexanderB. E. Schwenker and S. Marinai, Object detection and feature base learning with sparse convolutional neural networks, Artificial Neural Networks in Pattern Recognition, Proceedings, 4087 (2006), 221-232.

[3]

A. AmalL. S. LebaiV. Ibrahim and F.-Ma. Fernando, Towards a robust affect recognition: Automatic facial expression recognition in 3D faces, Expert Systems With Applications, 42 (2015), 3056-3066.

[4]

S. BashbaghiE. GrangerR. Sabourin and G. A. Bilodeau, Dynamic ensembles of exemplar-SVMs for still-to-video face recognition, Pattern Recognition, 69 (2017), 61-81.

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P. N. BelhumeurJ. P. Hespanha and D. Kriegman, Eigenfaces vs. fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1997), 711-720.

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D. Cai, X. He and J. Han, Semi-supervised discriminant analysis, IEEE 11th International Conference on Computer Vision, 2007, 1–7.

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F. L. CaoX. S. Feng and J. W. Zhao, Sparse representation for robust face recognition by dictionary decomposition, Journal of Visual Communication and Image Representation, 46 (2017), 260-268.

[8]

C. Cernazanu-Glavan and S. Holban, Segmentation of bone structure in x-ray images using convolutional neural network, Advances In Electrical And Computer Engineering, 13 (2013), 87-94.

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X. ChenS. XiangC.-L. Liu and C.-H. Pan, Vehicle detection in satellite images by hybrid deep convolutional neural networks, IEEE Geoscience and Remote Sensing Letters, 11 (2014), 1797-1801.

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M. De-la-TorreE. GrangerV. W. Radtke PauloR. Sabourin and O. Gorodnichy Dmitry, Partially-supervised learning from facial trajectories for face recognition in video surveillance, Information Fusion, 24 (2015), 31-53.

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S. ElaiwatM. BennamounF. Boussaid and A. El-Sallam, A Curve let-based approach for textured 3D face recognition, Pattern Recognition, 48 (2015), 1235-1246.

[12]

C. FangZ. Y. ZhaoP. Zhou and Z. C. Lin, Feature learning via partial differential equation with applications to face recognition, Pattern Recognition, 69 (2017), 14-25.

[13]

X. HeS. YanY. HuP. Niyogi and H.-J. Zhang, Face recognition using Laplacianfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (), 328-340.

[14]

N. HoangvuW. YangF. Shen and C. Sun, Kernel Low-Rank Representation for face recognition, Neurocomputing, 155 (2015), 32-42.

[15]

T. Hong-Phuc and M. Duranton, Paindavoine Michel, Efficient Data Encoding for Convolutional Neural Network application, ACM Transactions on Architecture and Code Optimization, 11 (2014), Article No. 49.

[16]

W. HwangX. HuangS. Z. Li and K. Junmo, Face recognition using Extended Curvature Gabor classifier bunch, Pattern Recognition, 48 (2015), 1247-1260.

[17]

W. L. Huang and H. J. Yin, Robust face recognition with structural binary gradient patterns, Pattern Recognition, 68 (2017), 126-140.

[18]

S. JiW. XuM. Yang and K. Yu, 3D convolutional neural networks for human action recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (2013), 221-231.

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J. JinK. Fu and C. Zhang, Traffic sign recognition with hinge loss trained convolutional neural networks, IEEE Transactions on Intelligent Transportation Systems, 15 (2014), 1991-2000.

[20]

F. JohannesM. Karlheinz and S. Johannes, A convolutional neural network tolerant of synaptic faults for low-power analog hardware, Artificial Neural Networks in Pattern Recognition, Proceedings, 4087 (2006), 122-132.

[21]

I.-J. Kim and X. Xie, Handwritten Hangul recognition using deep convolutional neural networks, International Journal on Document Analysis and Recognition, 18 (2015), 1-13.

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T. LarrainJ. S. BernhardD. Mery and K. W. Bowyer, Face recognition using sparse fingerprint classification algorithm, Ieee Transactions on Information Forensics and Security, 12 (2017), 1646-1657.

[23]

A. Levinskis, Convolutional neural network feature reduction using wavelet transform, Electronics & Electrical Engineering, 19 (2013), p61.

[24]

L. Li, M. Wang, X. Ding, J. Lian and Y. Zong, Convolutional neural network applied to traversability analysis of vehicles, Advances in Mechanical Engineering, 2013, Article No. 542832.

[25]

Y. LuN. ZengY. Liu and N. Zhang, A hybrid wavelet neural network and switching particle swarm optimization algorithm for face direction recognition, Neurocomputing, 155 (2015), 219-224.

[26]

Q. MaoM. DongZ. Huang and Y. Zhan, Learning salient features for speech emotion recognition using convolutional neural networks, IEEE Transactions On Multimedia, 16 (2014), 2203-2213.

[27]

S. RajbhandariZ. Ghassemlooy and M. Angelova, Adaptive soft' sliding block decoding of convolutional code using the artificial neural network, Transactions on Emerging Telecommunications Technologies, 23 (2012), 672-677.

[28]

T. N. SainathB. KingsburyG. SaonH. SoltauA.-R. MohamedG. Dahl and B. Ramabhadran, Deep convolutional neural networks for large-scale speech tasks, Neural Networks, 64 (2015), 39-48.

[29]

J. Shi and C. Qi, From local geometry to global structure: Learning latent subspace for low-resolution face image recognition, IEEE Signal Processing Letters, 22 (2015), 554-558.

[30]

P. SwietojanskiA. Ghoshal and S. Renals, Convolutional neural networks for distant speech recognition, IEEE Signal Processing Letters, 21 (2014), 1120-1124.

[31]

F. H. C. TiviveA. BouzerdoumI. Be KingJ. WangL. Chan and D. L. Wang, Rotation invariant face detection using convolutional neural networks, Neural Information Processing, PT 2, Proceedings, 4233 (2006), 260-269.

[32]

F. H. C. Tivive and A. Bouzerdoum, Efficient training algorithms for a class of shunting inhibitory convolutional neural networks, IEEE Transactions on Neural Networks, 16 (2005), 541-556.

[33]

U. C. Turhal and A. Duysak, Cross grouping strategy based 2DPCA method for face recognition, Applied Soft Computing, 29 (2015), 270-279.

[34]

M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, 3 (1991), 71-86.

[35]

S. N. ZengJ. P. Gou and L. M. Deng, An antinoise sparse representation method for robust face recognition via joint l(1) and l(2) regularization, Expert Systems with Applications, 82 (2017), 1-9.

[36]

W. ZhangR. LiH. DengL. WangW. LinS. Ji and D. Shen, Deep convolutional neural networks for multi-modality isointense infant brain image segmentation, Neuroimage, 108 (2015), 214-224.

[37]

X. Zhao, N. Evans and J. Dugelay, Semi-supervised face recognition with LDA self-training, 18th IEEE International Conference on Image Processing (ICIP), 2011, 3041–3044.

show all references

References:
[1]

O. Abdel-HamidA.-R. MohamedH. JiangL. DengG. Penn and D. Yu, Convolutional neural networks for speech recognition, IEEE-ACM Transactions on Audio Speech and Language Processing, 22 (2014), 1533-1545.

[2]

R. T. G. AlexanderB. E. Schwenker and S. Marinai, Object detection and feature base learning with sparse convolutional neural networks, Artificial Neural Networks in Pattern Recognition, Proceedings, 4087 (2006), 221-232.

[3]

A. AmalL. S. LebaiV. Ibrahim and F.-Ma. Fernando, Towards a robust affect recognition: Automatic facial expression recognition in 3D faces, Expert Systems With Applications, 42 (2015), 3056-3066.

[4]

S. BashbaghiE. GrangerR. Sabourin and G. A. Bilodeau, Dynamic ensembles of exemplar-SVMs for still-to-video face recognition, Pattern Recognition, 69 (2017), 61-81.

[5]

P. N. BelhumeurJ. P. Hespanha and D. Kriegman, Eigenfaces vs. fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1997), 711-720.

[6]

D. Cai, X. He and J. Han, Semi-supervised discriminant analysis, IEEE 11th International Conference on Computer Vision, 2007, 1–7.

[7]

F. L. CaoX. S. Feng and J. W. Zhao, Sparse representation for robust face recognition by dictionary decomposition, Journal of Visual Communication and Image Representation, 46 (2017), 260-268.

[8]

C. Cernazanu-Glavan and S. Holban, Segmentation of bone structure in x-ray images using convolutional neural network, Advances In Electrical And Computer Engineering, 13 (2013), 87-94.

[9]

X. ChenS. XiangC.-L. Liu and C.-H. Pan, Vehicle detection in satellite images by hybrid deep convolutional neural networks, IEEE Geoscience and Remote Sensing Letters, 11 (2014), 1797-1801.

[10]

M. De-la-TorreE. GrangerV. W. Radtke PauloR. Sabourin and O. Gorodnichy Dmitry, Partially-supervised learning from facial trajectories for face recognition in video surveillance, Information Fusion, 24 (2015), 31-53.

[11]

S. ElaiwatM. BennamounF. Boussaid and A. El-Sallam, A Curve let-based approach for textured 3D face recognition, Pattern Recognition, 48 (2015), 1235-1246.

[12]

C. FangZ. Y. ZhaoP. Zhou and Z. C. Lin, Feature learning via partial differential equation with applications to face recognition, Pattern Recognition, 69 (2017), 14-25.

[13]

X. HeS. YanY. HuP. Niyogi and H.-J. Zhang, Face recognition using Laplacianfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (), 328-340.

[14]

N. HoangvuW. YangF. Shen and C. Sun, Kernel Low-Rank Representation for face recognition, Neurocomputing, 155 (2015), 32-42.

[15]

T. Hong-Phuc and M. Duranton, Paindavoine Michel, Efficient Data Encoding for Convolutional Neural Network application, ACM Transactions on Architecture and Code Optimization, 11 (2014), Article No. 49.

[16]

W. HwangX. HuangS. Z. Li and K. Junmo, Face recognition using Extended Curvature Gabor classifier bunch, Pattern Recognition, 48 (2015), 1247-1260.

[17]

W. L. Huang and H. J. Yin, Robust face recognition with structural binary gradient patterns, Pattern Recognition, 68 (2017), 126-140.

[18]

S. JiW. XuM. Yang and K. Yu, 3D convolutional neural networks for human action recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (2013), 221-231.

[19]

J. JinK. Fu and C. Zhang, Traffic sign recognition with hinge loss trained convolutional neural networks, IEEE Transactions on Intelligent Transportation Systems, 15 (2014), 1991-2000.

[20]

F. JohannesM. Karlheinz and S. Johannes, A convolutional neural network tolerant of synaptic faults for low-power analog hardware, Artificial Neural Networks in Pattern Recognition, Proceedings, 4087 (2006), 122-132.

[21]

I.-J. Kim and X. Xie, Handwritten Hangul recognition using deep convolutional neural networks, International Journal on Document Analysis and Recognition, 18 (2015), 1-13.

[22]

T. LarrainJ. S. BernhardD. Mery and K. W. Bowyer, Face recognition using sparse fingerprint classification algorithm, Ieee Transactions on Information Forensics and Security, 12 (2017), 1646-1657.

[23]

A. Levinskis, Convolutional neural network feature reduction using wavelet transform, Electronics & Electrical Engineering, 19 (2013), p61.

[24]

L. Li, M. Wang, X. Ding, J. Lian and Y. Zong, Convolutional neural network applied to traversability analysis of vehicles, Advances in Mechanical Engineering, 2013, Article No. 542832.

[25]

Y. LuN. ZengY. Liu and N. Zhang, A hybrid wavelet neural network and switching particle swarm optimization algorithm for face direction recognition, Neurocomputing, 155 (2015), 219-224.

[26]

Q. MaoM. DongZ. Huang and Y. Zhan, Learning salient features for speech emotion recognition using convolutional neural networks, IEEE Transactions On Multimedia, 16 (2014), 2203-2213.

[27]

S. RajbhandariZ. Ghassemlooy and M. Angelova, Adaptive soft' sliding block decoding of convolutional code using the artificial neural network, Transactions on Emerging Telecommunications Technologies, 23 (2012), 672-677.

[28]

T. N. SainathB. KingsburyG. SaonH. SoltauA.-R. MohamedG. Dahl and B. Ramabhadran, Deep convolutional neural networks for large-scale speech tasks, Neural Networks, 64 (2015), 39-48.

[29]

J. Shi and C. Qi, From local geometry to global structure: Learning latent subspace for low-resolution face image recognition, IEEE Signal Processing Letters, 22 (2015), 554-558.

[30]

P. SwietojanskiA. Ghoshal and S. Renals, Convolutional neural networks for distant speech recognition, IEEE Signal Processing Letters, 21 (2014), 1120-1124.

[31]

F. H. C. TiviveA. BouzerdoumI. Be KingJ. WangL. Chan and D. L. Wang, Rotation invariant face detection using convolutional neural networks, Neural Information Processing, PT 2, Proceedings, 4233 (2006), 260-269.

[32]

F. H. C. Tivive and A. Bouzerdoum, Efficient training algorithms for a class of shunting inhibitory convolutional neural networks, IEEE Transactions on Neural Networks, 16 (2005), 541-556.

[33]

U. C. Turhal and A. Duysak, Cross grouping strategy based 2DPCA method for face recognition, Applied Soft Computing, 29 (2015), 270-279.

[34]

M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, 3 (1991), 71-86.

[35]

S. N. ZengJ. P. Gou and L. M. Deng, An antinoise sparse representation method for robust face recognition via joint l(1) and l(2) regularization, Expert Systems with Applications, 82 (2017), 1-9.

[36]

W. ZhangR. LiH. DengL. WangW. LinS. Ji and D. Shen, Deep convolutional neural networks for multi-modality isointense infant brain image segmentation, Neuroimage, 108 (2015), 214-224.

[37]

X. Zhao, N. Evans and J. Dugelay, Semi-supervised face recognition with LDA self-training, 18th IEEE International Conference on Image Processing (ICIP), 2011, 3041–3044.

Figure 1.  Internal structure of the convolutional neural networks
Figure 2.  Flowchart of the proposed CNN based human face recognition system
Figure 3.  Rate of face recognition on the test dataset of ORL database
Figure 4.  Rate of face recognition on the unlabeled training dataset of ORL database
Figure 5.  Rate of face recognition on the test dataset of Yale database
Figure 6.  Rate of face recognition on the unlabeled training dataset of Yale database
Figure 7.  Rate of face recognition on the test dataset of Extended Yale B database
Figure 8.  Rate of face recognition on the unlabeled training dataset of Extended Yale B database
Figure 9.  Average face recognition accuracy for different methods
Table 1.  Description of the CNN baseline model
Name Description
Input layer $3\times 24\times 24$ color RGB in the range $[0,1]$
Convolution 1 $3\times 5\times 5$ kernels, 64 output maps of $20\times 20$
Convolution 2 $64\times 5\times 5$ kernels, 64 output maps of $6\times 6$
Convolution 3 $64\times 3\times 3$ kernels, 64 output maps of $1\times 1$
Pooling 1 $2\times 2$ non-overlapping subsampling, 64 output maps of $10\times 10$
Pooling 2 $2\times 2$ non-overlapping subsampling, 64 output maps of $3\times 3$
Fully connected 1 HalfRect Units with 64 output neurons
Fully connected 2 10 output neurons
Name Description
Input layer $3\times 24\times 24$ color RGB in the range $[0,1]$
Convolution 1 $3\times 5\times 5$ kernels, 64 output maps of $20\times 20$
Convolution 2 $64\times 5\times 5$ kernels, 64 output maps of $6\times 6$
Convolution 3 $64\times 3\times 3$ kernels, 64 output maps of $1\times 1$
Pooling 1 $2\times 2$ non-overlapping subsampling, 64 output maps of $10\times 10$
Pooling 2 $2\times 2$ non-overlapping subsampling, 64 output maps of $3\times 3$
Fully connected 1 HalfRect Units with 64 output neurons
Fully connected 2 10 output neurons
Table 2.  The face recognition accuracy using the CMU PIE database (mean $\pm$ std.dev%)
Approach Unlabeled set Test set
Fisherface 21.8$\pm $1.5 21.8$\pm $1.5
Laplacianface 35.9$\pm $1.6 34.8$\pm $1.4
SDA 41.8$\pm $1.9 43.8$\pm $1.8
LDA self-training 48.9$\pm $2.2 50.3$\pm $2.1
CNN 36.8$\pm $4.7 38.7$\pm $4.6
The improved CNN 91.8$\pm $1.1 92.1$\pm $1.2
Approach Unlabeled set Test set
Fisherface 21.8$\pm $1.5 21.8$\pm $1.5
Laplacianface 35.9$\pm $1.6 34.8$\pm $1.4
SDA 41.8$\pm $1.9 43.8$\pm $1.8
LDA self-training 48.9$\pm $2.2 50.3$\pm $2.1
CNN 36.8$\pm $4.7 38.7$\pm $4.6
The improved CNN 91.8$\pm $1.1 92.1$\pm $1.2
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