[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Regularized Negative Label Relaxation Least Squares Regression for Face Recognition

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Least squares regression (LSR) is widely used for pattern classification. Some variants based on it try to enlarge the margin between different classes to achieve better performance. However, the large margin classifier doesn’t work well when it deals with the complex applications in the real world, such as face recognition, where images are captured with different facial expressions, lighting conditions or background. To address this problem, we propose a regularized negative label relaxation least squares regression method with the following characteristics. First, we introduce a negative \( \varepsilon \) dragging technique to relax the strict binary label matrix into a slack label matrix, which has more freedom to fit the labels and reduces the class margins at the same time. Second, we introduce manifold learning and class compactness graph to devise a regularization item to preserve the intrinsic structure of data and avoid the problem of overfitting. The class compactness graph can enable samples from the same class to be kept close together after they are transformed into the slack label space. The algorithm based on L2-norm loss function is devised. The experimental results show that our algorithm achieves better classification accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Xu Y, Li Z, Zhang B, Yang J, You J (2017) Sample diversity, representation effectiveness and robust dictionary learning for face recognition. Inf Sci 375:171–182. https://doi.org/10.1016/j.ins.2016.09.059

    Article  Google Scholar 

  2. Peng Y, Li L, Liu S, Li J, Wang X (2018) Extended sparse representation based classification method for face recognition. Mach Vis Appl 29(6):991–1007

    Google Scholar 

  3. Liu S, Li L, Jin M, Hou S, Peng Y (2019) Optimized coefficient vector and representation based classification methods for face recognition. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2960928

    Article  Google Scholar 

  4. Liu Z, Lai Z, Ou W et al (2020) Structured optimal graph based sparse feature extraction for semi-supervised learning. Sig Process. https://doi.org/10.1016/j.sigpro.2020.107456

    Article  Google Scholar 

  5. Xu Y, Zhong Z, Yang J, You J, Zhang D (2017) A New Discriminative Sparse Representation Method for Robust Face Recognition via ℓ2 Regularization. IEEE Trans Neural Netw 28(10):2233–2242

    MathSciNet  Google Scholar 

  6. Liu W, Zha Z, Wang Y, Lu K, Tao D (2016) p-Laplacian Regularized Sparse Coding for Human Activity Recognition. IEEE Trans Ind Electron 63(8):5120–5129

    Google Scholar 

  7. Gong C, Liu T, Tang Y, Yang J, Yang J, Tao D (2018) A regularization approach for instance-based superset label learning. IEEE Trans Syst Man Cybern 48(3):967–978

    Google Scholar 

  8. Yang Y, Liu Q, He X, Liu Z (2019) Cross-view multi-lateral filter for compressed multi-view depth video. IEEE Trans Image Process 28(1):302–315

    MathSciNet  MATH  Google Scholar 

  9. Liu S, Peng Y, Ben X, Yang W, Qiu G (2016) A novel label learning algorithm for face recognition. Sig Process 124:141–146

    Google Scholar 

  10. Fang Y, Wang J, Narwaria M, Callet PL, Lin W (2014) Saliency detection for stereoscopic images. IEEE Trans Image Process 23(6):2625–2636

    MathSciNet  MATH  Google Scholar 

  11. Zuo W, Wang P, Zhang D (2016) Comparison of three different types of wrist pulse signals by their physical meanings and diagnosis performance. IEEE J Biomed Health Inform 20(1):119–127

    Google Scholar 

  12. Peng Y, Li L, Liu S, Wang X, Li J (2018) Weighted constraint based dictionary learning for image classification. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2018.09.008

    Article  Google Scholar 

  13. Liu H, Xu B, Lu D et al (2018) A Path Planning Approach for Crowd Evacuation in Buildings Based on Improved Artificial Bee Colony Algorithm. Appl Soft Comput 68:360–376

    Google Scholar 

  14. Yang Y, Li B, Li P, Liu Q (2019) A two-stage clustering based 3d visual saliency model for dynamic scenarios. IEEE Trans Multimed 21(4):809–820

    Google Scholar 

  15. Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2932058

    Article  Google Scholar 

  16. Tu B, Zhang X, Kang X, Wang J, Benediktsson J (2019) Spatial density peak clustering for hyperspectral image classification with noisy labels. IEEE Trans Geosci Remote Sens 57(7):5085–5097

    Google Scholar 

  17. Peng Y, Ke J, Liu S, Li J, Lei T (2019) An improvement to linear regression classification for face recognition. Int J Mach Learn Cybernet 10(9):2229–2243

    Google Scholar 

  18. Du B, Xiong W, Wu J, Zhang L, Zhang L, Tao D (2017) Stacked convolutional denoising auto-encoders for feature representation. IEEE Trans Syst Man Cybern 47(4):1017–1027

    Google Scholar 

  19. Liu S, Peng Y, Sun Z, Wang X (2019) Self-calibration of projective camera based on trajectory basis. J Comput Sci 31:45–53

    Google Scholar 

  20. Lai Z, Xu Y, Yang J, Shen L, Zhang D (2017) Rotational invariant dimensionality reduction algorithms. IEEE Trans Syst Man Cybern 47(11):3733–3746

    Google Scholar 

  21. Liu Z, Wang J, Liu G et al (2019) Discriminative low-rank preserving projection for dimensionality reduction. Appl Soft Comput 85:105768

    Google Scholar 

  22. Yang W, Sun C, Zheng W (2016) A regularized least square based discriminative projections for feature extraction. Neurocomputing 175:198–205

    Google Scholar 

  23. Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced NetVLAD with and weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2019.2908982

    Article  Google Scholar 

  24. Tu B, Huang S, Fang L, Zhang G, Wang J, Zheng B (2018) Hyperspectral image classification via weighted joint nearest neighbor and sparse representation. IEEE J Sel Top Appl Earth Obs Remote Sens 11(11):4063–4075

    Google Scholar 

  25. Du B, Wang S, Wang N, Zhang L, Tao D, Zhang L (2016) Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach. Neurocomputing 204:153–161

    Google Scholar 

  26. Liu T, Tao D (2016) On the performance of manhattan nonnegative matrix factorization. IEEE Trans Neural Netw 27(9):1851–1863

    MathSciNet  Google Scholar 

  27. Yang J, Zhu Y, Li K, Yang J, Hou C (2018) Tensor completion from structurally-missing entries by low-tt-rankness and fiber-wise sparsity. IEEE J Sel Top Signal Process 12(6):1420–1434

    Google Scholar 

  28. Li K, Dai Q, Xu W et al (2012) Temporal-dense dynamic 3D reconstruction with low frame rate cameras”. IEEE J Sel Top Signal Process 6(5):447–459

    Google Scholar 

  29. Liu S, Peng Y (2012) A local region-based Chan-Vese model for image segmentation. Pattern Recogn 45(7):2769–2779

    MATH  Google Scholar 

  30. Peng Y, Liu S, Qian Y, Wu X, Hong L (2019) A local mean and variance active contour model for biomedical image segmentation. J Comput Sci 33:11–19

    MathSciNet  Google Scholar 

  31. Xu Y, Fang X, Li X, Yang J, You J, Liu H, Teng S (2014) Data Uncertainty in Face Recognition. IEEE Trans Syst Man Cybern 44(10):1950–1961

    Google Scholar 

  32. Du B, Zhang M, Zhang L, Hu R, Tao D (2017) Pltd: patch-Based low-rank tensor decomposition for hyperspectral images. IEEE Trans Multimed 19(1):67–79

    Google Scholar 

  33. Gong C, Fu K, Loza A, Wu Q, Liu J, Yang J (2014) Pagerank tracker: from ranking to tracking. IEEE Trans Syst Man Cybern 44(6):882–893

    Google Scholar 

  34. Gong C, Tao D, Maybank SJ, Liu W, Kang G, Yang J (2016) Multi-modal curriculum learning for semi-supervised image classification. IEEE Trans Image Process 25(7):3249–3260

    MathSciNet  MATH  Google Scholar 

  35. Fang Y, Fang Z, Yuan F, Yang Y, Yang S, Xiong N (2017) Optimized multioperator image retargeting based on perceptual similarity measure. IEEE Trans Syst Man Cybern 47(11):2956–2966

    Google Scholar 

  36. Du B, Zhang L (2011) Random-Selection-Based Anomaly Detector for Hyperspectral Imagery. IEEE Trans Geosci Remote Sens 49(5):1578–1589

    Google Scholar 

  37. Ding C, Tao D (2016) A comprehensive survey on pose-invariant face recognition. ACM Trans Intell Syst Technol 7(3):1–42

    Google Scholar 

  38. Lai Z, Wong WK, Xu Y, Yang J, Zhang D (2016) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw 27(4):723–735

    MathSciNet  Google Scholar 

  39. Xu Y, Fei L, Zhang D (2015) Combining left and right palmprint images for more accurate personal identification. IEEE Trans Image Process 24(2):549–559

    MathSciNet  MATH  Google Scholar 

  40. Gong C, Liu T, Tao D, Fu K, Tu E, Yang J (2015) Deformed graph laplacian for semisupervised learning. IEEE Trans Neural Netw 26(10):2261–2274

    MathSciNet  Google Scholar 

  41. Xu Y, Zhang Z, Lu G, Yang J (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recogn 54(54):68–82

    Google Scholar 

  42. Fang Y, Wang Z, Lin W, Fang Z (2014) Video saliency incorporating spatiotemporal cues and uncertainty weighting. IEEE Trans Image Process 23(9):3910–3921

    MathSciNet  MATH  Google Scholar 

  43. Peng Y, Li L, Liu S, Lei T (2018) Space-frequency domain based joint dictionary learning and collaborative representation for face recognition. Sig Process 147:101–109

    Google Scholar 

  44. Yang W, Zhang X, Li J (2020) A Local Multiple Patterns Feature Descriptor for Face Recognition. Neurocomputing 373:109–122

    Google Scholar 

  45. Liu H, Liu B, Zhang H et al (2018) Crowd evacuation simulation approach based on navigation knowledge and two-layer control mechanism. Inf Sci 436–437:247–267

    MathSciNet  Google Scholar 

  46. Nie F, Wang H, Huang H, Ding C (2013) Adaptive Loss Minimization for Semi-Supervised Elastic Embedding. In: The Twenty-Third international joint conference on Artificial Intelligence pp 1565–1571

  47. Tu B, Zhou C, Kuang W, Guo L, Ou X (2018) Hyperspectral imagery noisy label detection by spectral angle local outlier factor. IEEE Geosci Remote Sens Lett 15(9):1417–1421

    Google Scholar 

  48. Yang W, Li J, Zheng H, Xu R (2018) A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction. IEEE Access 6:7445–7451

    Google Scholar 

  49. Yang J, Gan Z, Li K, Hou C (2015) Graph-based segmentation for RGB-D data using 3-D geometry enhanced superpixels. IEEE Trans Cybern 45(5):913–926

    Google Scholar 

  50. Fang X, Xu Y, Li X, Lai Z, Wong W (2015) Learning a nonnegative sparse graph for linear regression. IEEE Trans Image Process 24(9):2760–2771

    MathSciNet  MATH  Google Scholar 

  51. Liu S, Li L, Peng Y, Qiu G, Lei T (2017) Improved sparse representation method for image classification. IET Comput Vis 11(4):319–330

    Google Scholar 

  52. Fan Z, Xu Y, Zhang D (2011) Local linear discriminant analysis framework using sample neighbors. IEEE Trans Neural Netw 22(7):1119–1132

    Google Scholar 

  53. Gong C, Tao D, Liu W, Liu L, Yang J (2017) Label propagation via teaching-to-learn and learning-to-teach. IEEE Trans Neural Netw 28(6):1452–1465

    Google Scholar 

  54. Łȩski J (2003) Ho–Kashyap classifier with generalization control. Pattern Recogn Lett 24(14):2281–2290

    MATH  Google Scholar 

  55. Xiang S, Nie F, Meng G, Pan C, Zhang C (2012) discriminative least squares regression for multiclass classification and feature selection. IEEE Trans Neural Netw 23(11):1738–1754

    Google Scholar 

  56. Du B, Wei Q, Liu R (2019) An improved quantum-behaved particle swarm optimization for endmember extraction. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2019.2903875

    Article  Google Scholar 

  57. Xu Y, Lu Y (2015) Adaptive Weighted Fusion. Neurocomputing 168:566–574

    Google Scholar 

  58. Lai Z, Xu Y, Jin Z, Zhang D (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662

    Google Scholar 

  59. Yang W, Zhou L, Li T, Wang H (2019) A face detection method based on cascade convolutional neural network. Multimed Tools Appl 78(17):24373–24390

    Google Scholar 

  60. Tu B, Yang X, Li N, Zhou C, He D (2020) Hyperspectral anomaly detection via density peak clustering. Pattern Recogn Lett 129:144–149

    Google Scholar 

  61. Peng Y, Liu S, Lei T, Li J, Guo M (2018) Negative ε dragging technique for pattern classification. IEEE Access 6:488–494

    Google Scholar 

  62. Peng Y, Zhang L, Liu S, Wang X, Guo M (2017) Kernel negative ε dragging linear regression for pattern classification. Complexity 2691474:1–14

    Google Scholar 

  63. Liu W, Liu H, Tao D, Wang Y, Lu K (2015) Multiview Hessian regularized logistic regression for action recognition. Sig Process 110:101–107

    Google Scholar 

  64. Nie F, Huang H, Cai X, Ding C (2010) Efficient and robust feature selection via joint ℓ2, 1-Norms Minimization. In: 24th Annual conference on neural information processing systems vol 23, pp 1813–1821

  65. Du B, Zhang L, Zhang L, Chen T, Wu K (2012) A discriminative manifold learning based dimension reduction method for hyperspectral classification. Int J Fuzzy Syst 14(2):272–277

    Google Scholar 

  66. Peng Y, Sehdev P, Liu S, Li J, Wang X (2018) l2,1-norm minimization based negative label relaxation linear regression for feature selection. Pattern Recogn Lett 116:170–178

    Google Scholar 

  67. Liu W, Zhang L, Tao D, Cheng J (2017) Support vector machine active learning by hessian regularization. J Vis Commun Image Represent 49:47–56

    Google Scholar 

  68. Tu B, Zhou C, He D, Huang S, Plaza A (2019) Hyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2019.2961141

    Article  Google Scholar 

  69. Peng Y, Liu S, Wang X, Wu X (2019) Joint local constraint and fisher discrimination based dictionary learning for image classification. Nuerocomputing. https://doi.org/10.1016/j.neucom.2019.05.103

    Article  Google Scholar 

  70. Li X, Lin S, Yan S, Xu D (2008) Discriminant locally linear embedding with high-order tensor data. IEEE Trans Cybern 38(2):342–352

    Google Scholar 

  71. He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Google Scholar 

  72. Peng Y, Li L, Liu S, Lei T, Wu J (2018) A new virtual samples-based CRC method for face recognition. Neural Process Lett 48:313–327

    Google Scholar 

  73. Liu W, Yang X, Tao D, Cheng J, Tang Y (2018) Multiview dimension reduction via Hessian multiset canonical correlations. Inf Fus 41:119–128

    Google Scholar 

  74. Tenenbaum J, Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key R&D Program of China (No. 2017YFB1402102), the National Natural Science Foundation of China (Nos. 61873155, 61672333, 61703096, 11772178), Transfer and Promotion Plan of Scientific and Technological Achievements of Shaanxi Province (No. 2019CGXNG-019), the National Natural Science Foundation of Shaanxi Province (No. 2018JM6050), Innovation Chain of Key Industries of Shaanxi Province (No. 2019ZDLSF07-01), the Key Science and Technology Program of Shaanxi Province, (No. 2016GY-081).

Author information

Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Kai He & Yali Peng

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Kai He, Yali Peng & Shigang Liu

  3. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210046, China

    Jun Li

Authors
  1. Kai He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yali Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shigang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yali Peng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, K., Peng, Y., Liu, S. et al. Regularized Negative Label Relaxation Least Squares Regression for Face Recognition. Neural Process Lett 51, 2629–2647 (2020). https://doi.org/10.1007/s11063-020-10219-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10219-6

Keywords

Search

Navigation