Face recognition under unconstrained based on LBP and deep learning

Tongxin Xuebao/Journal on Communications

Volumn 35, Issue 6, 2014, Pages 154-160

  Liang, Shu Fen ^a   Liu, Yin Hua ^a   Li, Li Chen ^a  

^a Wuyi University   (China)

Author keywords

Deep learning; Deep network; Face recognition; LBP; Unconstrained condition

Indexed keywords

NETWORK LAYERS; TEXTURES;

DEEP LEARNING; FACE IMAGES; FACE RECOGNITION METHODS; LBP; LBP TEXTURES; NETWORK PARAMETERS; TEST SAMPLES; UNCONSTRAINED CONDITION;

FACE RECOGNITION;

EID: 84904393686     PISSN: 1000436X     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.1000-436x.2014.06.020     Document Type: Article

References (17)

1. MEDIONI G, CHOI J, KUO C H, et al. Identifying noncooperative subjects at a distance using face images and inferred three dimensional face models [J]. IEEE Trans Syst, Man, Cybern A, Syst, Humans, 2009, 39(1): 12-24.
2. BLANZ V, VETTER T. Face recognition based on fitting a 3D morphable model [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003, 25(9): 1063-1074.
3. LIOR W, TAL H, YANIV T. Effective unconstrained face recognition by combining multiple descriptors and learned background statistics [J]. IEEE Pattern Analysis and Machine Intelligence, 2011, 33(10): 1978-1990.
4. MARSICO M D E, NAPPI M, RICCO D. Robust face recognition for uncontrolled pose and illumination changes [J]. IEEE Transactions on Systems, Man and Cybernetic, 2012, 43(1): 149-163.
5. JAVIER R, RODRIGO V, MAURICIO C. Recognition of faces in unconstrained environments: a comparative study [J]. Journal on Advances in Signal Processing. 2009, 12(4): 44-69.
6. WOLF L, HASSNER T, TAIGMAN Y. Descriptor based methods in the wild [A]. Faces in Real-life Images Workshop in ECCV [C]. 2008. 1-14
7. ZHAO D, LIN Z, XIAO R, et al. Linear laplacian discrimination for feature extraction [A]. Proc IEEE Conference on Computer Vision and Pattern Recognition [C]. 2009. 1-7.
8. BENGIO Y, DELALLEAU O. On the expressive power of deep architectures [A]. Proc of 14th International Conference on Discovery Science [C]. Berlin: Springer-Verlag, 2011. 18-36.
9. HINTON G E, OSINDERO S, THE Y-W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18(7): 1527-1554.
10. COTTRELL G W. New life for neural networks [J]. Science, 2006, 313(5786): 454-455.
11. TALOR G W, HINTON G E, ROWEIS S T. Modeling human motion using binary latent variables [A]. Advances in Neural Information Processing Systems [C]. 2007. 1345-1352
12. ITAMAR A, DEREK C R, THOMAS P K. Deep machine learning-a new frontier [J]. Artificial Intelligence Research IEEE Computational Intelligence Magazine, 2010, 5(4): 13-18
13. OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
14. ZHAO Z, PIETIKAINEN M. Dynamic texture recognition using local binary patterns with an application to facial expressions [J]. IEEE Trans PAMI, 2007, 27(6): 915-928.
15. LEI Z, LIAO S. Face recognition by exploring information jointly in space, scale and orientation [J]. IEEE Trans on Image Processing, 2011, 20(1): 247-256.
16. BENGIO Y. Learning deep architectures for AI [J]. Foundations and Trends in Machine Learning, 2009, 2(1): 1-127.
17. HINTON G E. Training products of experts by minimizing contrastive divergence [J]. Neural Computation, 2002, 14(8): 1771-1800.