The face recognition based on ensemble method
Article No.: 22, Pages 1 - 5
Abstract
In this paper, we study the performance of different deep convolutional networks in 1:N face recognition and process the feature comparison in order to obtain a higher face recognition rate. The improved feature comparison method is proposed: using Basis Pursuit De-Noising (BPDN) method combined with Homotopy algorithm to replace the nearest neighbor analysis in 1:N face recognition. Based on the Facenet model, we also compare the performance of different loss functions in face verification and recognition. Furthermore, the ensemble method and voting classifier are used to integrate Facenet, InsightFace and BPDN method to realize 1:N face recognition. It should be noted that the 1:N dataset is transformed from Labeled Faces in the Wild (LFW) dataset, including the 1 dataset and the N dataset. The 1 dataset and the N dataset contain 1680 faces and 5749 faces respectively, one for each person. The final experiments show that the ensemble method's accuracy is 82.98%, which is higher than the above methods, and indicate the ensemble method we proposed can improve the face recognition rate.
References
[1]
Schroff F, Kalenichenko D and Philbin J (2015). IEEE 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Boston, MA, USA (2015.6.7-2015.6.12)] 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - FaceNet: A unified embedding for face recognition and clustering, =815--823.
[2]
Huang G B, Ramesh M, Berg T and Learned-Miller (2007). Labeled faces in the wild: A database for studying facerecognition in unconstrained environments. Technical report.
[3]
Szegedy C, Ioffe S, Vanhoucke V, et al. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. National conference on artificial intelligence, 4278--4284.
[4]
Deng J, Guo J, Xue N, et al. (2018). Arcface: Additive angular margin loss for deep face recognition. arXiv preprint arXiv:1801.07698.
[5]
Hu C, Lu X, Liu P, Jing X and Yue D (2019). Single Sample Face Recognition Under Varying Illumination via QRCP Decomposition. IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2624--2638, May 2019.
[6]
Hu C, Ye M, Ji S, Zeng W and Lu X (2015). A new face recognition method based on image decomposition for single sample per person problem. Neurocomputing, 160, 287--299.
[7]
Hu C, Lu X, Ye M and Zeng W (2017). Singular value decomposition and local near neighbors for face recognition under varying illumination. Pattern Recognition, 2017, 64, 60--83.
[8]
Zhang K, Zhang Z, Li Z, et al. (2016). Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks. IEEE Signal Processing Letters, 23(10), 1499--1503.
[9]
Figueiredo M A, Nowak R D, Wright S J, et al. (2007). Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems. IEEE Journal of Selected Topics in Signal Processing, 1(4), 586--597.
[10]
Simonyan K and Zisserman A (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. international conference on learning representations.
[11]
He K, Zhang X, Ren S, et al. (2016). Deep Residual Learning for Image Recognition. Computer vision and pattern recognition, 2016, 770--778.
[12]
Iandola F N, Han S, Moskewicz M W, et al. (2017). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv: Computer Vision and Pattern Recognition.
[13]
Zhang X, Zhou X, Lin M, et al. (2018). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Computer vision and pattern recognition, 6848--6856.
[14]
Howard A, Zhu M, Chen B, et al. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv: Computer Vision and Pattern Recognition.
[15]
Wen Y, Zhang K, Li Z, et al. (2016). A Discriminative Feature Learning Approach for Deep Face Recognition. European conference on computer vision, 499--515.
[16]
Liu W, Wen Y, Yu Z, et al. (2017). SphereFace: Deep Hypersphere Embedding for Face Recognition. Computer vision and pattern recognition, 6738--6746.
[17]
Liu W, Wen Y, Yu Z, et al. (2016). Large-margin softmax loss for convolutional neural networks. ICML, 2(3), 7.
[18]
Feng W, Jian C, Liu W, et al. (2018). Additive Margin Softmax for Face Verification. IEEE Signal Processing Letters, 25(7), 926--930.
[19]
Lu W and Vaswani N (2012). Regularized modified BPDN for noisy sparse reconstruction with partial erroneous support and signal value knowledge. IEEE Transactions on Signal Processing, 60(1), 182--196.
[20]
Yi D, Lei Z, Liao S, et al. (2014). Learning Face Representation from Scratch. arXiv: Computer Vision and Pattern Recognition.
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December 2019
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- ASciE: Association for Science and Engineering
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 December 2019
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- Research-article
Funding Sources
- China Postdoctoral Science Foundation
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province
- Postdoctoral Research Funding Program of Jiangsu Province
- NUPTSF
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AIIPCC '19
Sponsor:
- ASciE
AIIPCC '19: 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing
December 19 - 21, 2019
Sanya, China
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AIIPCC '19 Paper Acceptance Rate 78 of 211 submissions, 37%;
Overall Acceptance Rate 78 of 211 submissions, 37%
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