Browse > Article
http://dx.doi.org/10.3837/tiis.2019.12.013

Facial Feature Recognition based on ASNMF Method  

Zhou, Jing (School of Mathematics and Computer Science, Jianghan University)
Wang, Tianjiang (School of Computer Science and Technology, HuaZhong University of Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.12, 2019 , pp. 6028-6042 More about this Journal
Abstract
Since Sparse Nonnegative Matrix Factorization (SNMF) method can control the sparsity of the decomposed matrix, and then it can be adopted to control the sparsity of facial feature extraction and recognition. In order to improve the accuracy of SNMF method for facial feature recognition, new additive iterative rules based on the improved iterative step sizes are proposed to improve the SNMF method, and then the traditional multiplicative iterative rules of SNMF are transformed to additive iterative rules. Meanwhile, to further increase the sparsity of the basis matrix decomposed by the improved SNMF method, a threshold-sparse constraint is adopted to make the basis matrix to a zero-one matrix, which can further improve the accuracy of facial feature recognition. The improved SNMF method based on the additive iterative rules and threshold-sparse constraint is abbreviated as ASNMF, which is adopted to recognize the ORL and CK+ facial datasets, and achieved recognition rate of 96% and 100%, respectively. Meanwhile, from the results of the contrast experiments, it can be found that the recognition rate achieved by the ASNMF method is obviously higher than the basic NMF, traditional SNMF, convex nonnegative matrix factorization (CNMF) and Deep NMF.
Keywords
scale normalization; improved iterative step sizes; improved iterative rules; threshold sparse; facial feature extraction; ASNMF method;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. Petersen, M. Nielsen and S. S. Brandt, "Conditional Point Distribution Models," in Proc. of International MICCAI Workshop on Medical Computer Vision, pp. 1-10, September, 2011.
2 A. Dewan, T. Caselitz, G. D. Tipaldi and W. Burgard, "Motion-based detection and tracking in 3D LiDAR scans," in Proc. of IEEE International Conference on Robotics and Automation(ICRA), pp. 4508-4513, May 16-21, 2016.
3 A. Lowhur and M. C. Chuah, "Dense Optical Flow Based Emotion Recognition Classifier," in Proc. of IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems, pp. 573-578, October 19-22, 2015.
4 F. Liu, J. Tang, Y. Song, Y. Bi, and S. Yang, "Local structure based multi-phase collaborative representation for face recognition with single sample per person," Information Sciences, vol. 346, pp. 198-215, June, 2016.   DOI
5 M. S. S. Kumar, R. Swami and M. Karuppiah, "An Improved Face Recognition Technique Based on Modular LPCA Approach," Journal of Computer Science, vol. 7, no. 12, pp. 1900-1907, December, 2011.   DOI
6 L. A. Camentab, F. J. Galdames, K. W. Bowyer and C. A. Perez, "Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models," Pattern Recognition, vol. 48, no. 11, pp. 3371-3384, November, 2015.   DOI
7 F. Artoni, A. Delorme and S. Makeig, "Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition," NeuroImage, vol. 175, pp. 176-187, July, 2018.   DOI
8 F. Artoni, A. Delorme, S. Makeig, "A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalographic preprocessing pipelines," Data in Brief, vol. 22, pp. 787-793, February, 2019.   DOI
9 Y. Pang, Y. He, Y. Yuan and K. Wang, "Robust CoHOG Feature Extraction in Human-Centered Image/Video Management System," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 458-468, April, 2012.   DOI
10 A. Pillai, R. Soundrapandiyan, S. Satapathy, S. C. Satapathy, K. H. Jun and R. Krishnan, "Local diagonal extrema number pattern: A new feature descriptor for face recognition," Future Generation Computer Systems, vol. 81, pp. 297-306, April, 2018.   DOI
11 D. D. Lee and H. S. Seung, "Algorithms for non-negative matrix factorization," Advances in Neural Information Processing Systems (NIPS), vol. 1, pp. 556-562, 2001.
12 P. Liu, J. M. Guo, K. Chamnongthai and H. Prasetyo, "Fusion of color histogram and LBP-based features for texture image retrieval and classification," Information Sciences, vol. 390, no. 1, pp. 95-111, June, 2017.   DOI
13 S. N. Borade, R. R. Deshmukh and S. Ramu, "Face recognition using fusion of PCA and LDA: Borda count approach," in Proc. of Mediterranean Conference on Control and Automation, IEEE press, pp.21-24, June 21-24, 2016.
14 Z. Li, J. Tang and X. He, "Robust Structured Nonnegative Matrix Factorization for Image Representation," IEEE Trans. on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1947-1960, May, 2018.   DOI
15 Y. Lu, C. Yuan, W. Zhu and X. Li, "Structurally incoherent low-rank nonnegative matrix factorization for image classification," IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5248-5260, November, 2018.   DOI
16 Z. X. Guo, S. H. Zhang, "Sparse Deep Nonnegative Matrix Factorization," arXiv:1707.093 16v1[cs.CV], pp. 1-13, July, 2017.
17 Y. Chen, T. Z. Huang, X. L. Zhao and L. J. Deng, "Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization," Applied Mathematical Modelling, vol. 63, pp. 128-147, November, 2018.   DOI
18 X. Li, L. Wang, Q. Cheng, P. Wu, W. Gan, L. Fang, "Cloud removal in remote sensing images using nonnegative matrix factorization and error correction," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 148, pp. 103-113, February, 2019.   DOI
19 S. Z. Lai, "Research on several non-negative matrix factorization methods and applications," A Master Thesis of University of Electronic Science and Technology, pp. 17-29, June, 2014.
20 G. S. Cui, X. L. Li, Y. S. Dong, "Subspace clustering guided convex nonnegative matrix factorization," Neurocomputing, vol. 292, pp. 38-48, May, 2018.   DOI
21 Y. Wang, "Proximal gradient method for convex and Semi-Nonnegative matrix factorization," A Dissertation of Northeast Normal University for Master Degree, pp. 5-6, June, 2015.
22 Q. L. Ye, J. Yang, F. Liu, C. X. Zhao, N. Ye and T. M. Yin, "L1-norm distance linear discriminant analysis based on an effective iterative algorithm," IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 1, pp. 114-129, August, 2018.   DOI
23 P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," Computer Vision and Pattern Recognition (CVPR), vol.1, pp. 511-518, December 8-14, 2001.