[1]SUN Jinguang,MENG Fanyu.Face recognition by weighted fusion of facial features[J].CAAI Transactions on Intelligent Systems,2015,10(6):912-920.[doi:10.11992/tis.201509025]
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Face recognition by weighted fusion of facial features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 6 Page number: 912-920 Column: 学术论文—机器感知与模式识别 Public date: 2015-12-25
Title:
Face recognition by weighted fusion of facial features
Author(s):
SUN Jinguang1; 2;  MENG Fanyu2
1. School of Electronics and Information Engineering, Liaoniing Technical University, Huludao 125000, China;
2. LiaoNing Digital Mining Equipment Engineering Technology Research Center, Fuxin 123000, China
Keywords:
face recognition; unconstrained condition; deep belief networks; local feature; feature fusion; overall feature
CLC:
TN911.73
DOI:
10.11992/tis.201509025
Abstract:
The accuracy of face recognition is low under unconstrained conditions. To solve this problem, we propose a new method based on deep learning and the weighted fusion of facial features. First, we divide facial feature points into five regions using an active shape model and then sample different facial components corresponding to those facial feature points. A corresponding deep belief network(DBN) was then trained based on these regional samples to obtain optimal network parameters. The five regional sampling regions and entire facial image obtained were then inputted into a corresponding neural network to adjust the network weight and complete the construction of sub-networks. Finally, using softmax regression, we obtained six similarity vectors of different components. These six similarity vectors comprise a similarity matrix, which is then multiplied by the weight vector to derive the final recognition result. Recognition accuracy was 97% and 91.63% on the ORL and WFL face databases, respectively. Compared with traditional recognition algorithms such as SVM, DBN, PCA, and FIP+LDA, recognition rates for both databases were improved in both constrained and unconstrained conditions. On the basis of these experimental results, we conclude that the proposed algorithm demonstrates high efficiency in face recognition.
References:
[1] TURK M, PENTLAND A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86.
[2] SUN Yi, WANG Xiaogang, TANG Xiaoou. Deep learning face representation from predicting 10, 000 classes[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1891-1898.
[3] HU Junlin, LU Jiwen, TAN Y P. Discriminative deep metric learning for face verification in the wild[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1875-1882.
[4] HUANG G B, LEE H, LEARNED-MILLER E. Learning hierarchical representations for face verification with convolutional deep belief networks[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2012:2518-2525.
[5] ZHU Zhenyao, LUO Ping, WANG Xiaogang, et al. Deep learning identity-preserving face space[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW, Australia, 2013:113-120.
[6] SUN Yi, WANG Xiaogang, TANG Xiaoou. Hybrid deep learning for face verification[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW, Australia, 2013:1489-1496.
[7] TAIGMAN Y, YANG Ming, RANZATO M A, et al. Deepface:Closing the gap to human-level performance in face verification[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1701-1708.
[8] SUN Yi, CHEN Yuheng, WANG Xiaogang, et al. Deep learning face representation by joint identification-verification[J]. Advances in Neural Information Processing Systems. 2014.
[9] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.
[10] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[11] AREL I, ROSE D C, KARNOWSKI T P. Deep machine learning a new frontier in artificial intelligence research[J]. IEEE Computational Intelligence Magazine, 2010, 5(4):13-18.
[12] COOTES T F, TAYLOR C J, COOPER D H, et al. Active shape models-their training and application[J]. Computer Vision and Image Understanding, 1995, 61(1):38-59.
[13] MILBORROW S, NICOLLS F. Active shape models with SIFT descriptors and MARS[J]. VISAPP, 2014, 1(2):5.
[14] MILBORROW S, BISHOP T E, NICOLLS F. Multiview active shape models with SIFT descriptors for the 300-W face landmark challenge[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops. Sydney, NSW, Australia, 2013:378-385.
[15] Bengio Y, Delalleau O. On the expressive power of deep architectures[C]//Proceedings of the 22nd International Conference. Espoo, Finland, 2011:18-36.
[16] HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8):1771-1800.
[17] BENGIO Y. Learning deep architectures for AI[J]. Foundations and Trends in Machine Learning, 2009, 2(1):1-127.
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