Multi-Resolution Dictionary Learning Algorithm with Discriminative Locality Constraints for Face Recognition

Shuying Zeng; Hongzhong Tang; Shijun Deng; Dongbo Zhang

doi:10.3788/LOP202158.1415008

[1] Cheng C, Da F P, Wang C X et al. Pose invariant face recognition using maximum Gabor similarity based on Lucas-Kanade algorithm[J]. Acta Optica Sinica, 39, 0715005(2019).

[2] Liu F, Li M J, Hu J W et al. Expression recognition based on low pixel face images[J]. Laser & Optoelectronics Progress, 57, 101008(2020).

[3] Chen Y F, Su J B. Sparse embedded dictionary learning on face recognition[J]. Pattern Recognition, 64, 51-59(2017).

[4] Cai J X, Feng G C, Tang X et al. Human action recognition by leaning pose dictionary[J]. Acta Optica Sinica, 34, 1215002(2014).

[5] Li F Y, Huo H T, Li J et al. Hyperspectral image classification via multiple-feature-based improved sparse representation[J]. Acta Optica Sinica, 39, 0528004(2019).

[6] Wright J, Yang A Y, Ganesh A et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 210-227(2009).

[7] Zhang Q, Li B X. Discriminative K-SVD for dictionary learning in face recognition[C]. //2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA., 2691-2698(2010).

[8] Jiang Z L, Lin Z, Davis L S. Label consistent K-SVD: learning a discriminative dictionary for recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 2651-2664(2013).

[9] Yang M, Zhang L, Feng X C et al. Fisher discrimination dictionary learning for sparse representation[C]. //2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain., 543-550(2011).

[10] Gu S, Zhang L, Zuo W et al. Projective dictionary pair learning for pattern classification[C]. //Advances in Neural Information Processing Systems, December 8-13, 2014, Montreal, 793-801(2014).

[11] Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 15, 1373-1396(2003).

[12] Yu K, Zhang T, Gong Y. Nonlinear learning using local coordinate coding[C]. //Advances in Neural Information Processing Systems, December 7-10, 2009, Vancouver, British Columbia, 2223-2231(2009).

[13] Min H Q, Liang M J, Luo R H et al. Laplacian regularized locality-constrained coding for image classification[J]. Neurocomputing, 171, 1486-1495(2016).

[14] Guo J, Guo Y Q, Li Y et al. Locality sensitive discriminative dictionary learning[C]. //2015 IEEE International Conference on Image Processing (ICIP), September 27-30, 2015, Quebec City, QC, Canada., 1558-1562(2015).

[15] Li Z M, Lai Z H, Xu Y et al. A locality-constrained and label embedding dictionary learning algorithm for image classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 28, 278-293(2017).

[16] Zhang Z, Sun Y L, Wang Y et al. Twin-incoherent self-expressive locality-adaptive latent dictionary pair learning for classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 32, 947-961(2021).

[17] Taigman Y, Yang M, Ranzato M et al. DeepFace: closing the gap to human-level performance in face verification[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA, 1701-1708(2014).

[18] Sun Y, Wang X G, Tang X O. Deep learning face representation from predicting 10,000 classes[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 1891-1898(2014).

[19] Luo X L, Xu Y, Yang J. Multi-resolution dictionary learning for face recognition[J]. Pattern Recognition, 93, 283-292(2019).

[20] Zhang Y J, Zheng S J, Zhang X X et al. Multi-resolution dictionary learning method based on sample expansion and its application in face recognition[J]. Signal, Image and Video Processing, 15, 307-313(2021).

[21] Sadeghi M, Babaie Z M, Jutten C. Learning overcomplete dictionaries based on atom-by-atom updating[J]. IEEE Transactions on Signal Processing, 62, 883-891(2014).

[22] Bottou L. Large-scale machine learning with stochastic gradient descent[C]. //Proceedings of COMPSTAT 2010, August 22-27, 2010, Paris, France. [S.l.]: Physica-Verlag HD, 177-186(2010).

[23] Samaria F S, Harter A C. Parameterisation of a stochastic model for human face identification[C]. //Proceedings of 1994 IEEE Workshop on Applications of Computer Vision, December 5-7, 1994, Sarasota, FL, USA., 138-142(1994).

[24] Georghiades A S, Belhumeur P N, Kriegman D J. From few to many: illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 643-660(2001).

[25] Martinez A, Benavente R. The AR face database[R]. Barcelona: Barcelona Computer Vision Center(CVC) Technical, 24(1998).

[26] Sim T, Baker S, Bsat M. The CMU pose, illumination, and expression (PIE) database[J]. Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, 53-58(2002).

[27] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[28] Adelson E H, Anderson C H, Bergen J R. Pyramid methods in image processing[J]. RCA Engineer, 29, 33-41(1984).