Fast Face Recognition Via Sparse Coding and Extreme Learning Machine

Cognitive Computation

Volumn 6, Issue 2, 2014, Pages 264-277

  He, Bo ^a   Xu, Dongxun ^a   Nian, Rui ^a   van Heeswijk, Mark ^b   Yu, Qi ^b   Miche, Yoan ^b   Lendasse, Amaury ^b,c,d  

^a OCEAN UNIVERSITY OF CHINA   (China)

^b AALTO UNIVERSITY   (Finland)

^c BASQUE FOUNDATION FOR SCIENCE   (Spain)

^d UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Common feature hypothesis; Extreme learning machine; Face recognition; Sparse coding

Indexed keywords

CODES (SYMBOLS); IMAGE CODING; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; NETWORK LAYERS;

COMMON FEATURES; COMPUTATIONAL COSTS; EXTREME LEARNING MACHINE; FEED-FORWARD NETWORK; GLOBAL REPRESENTATION; INFORMATION EMBEDDING; SPARSE CODING; STATE-OF-THE-ART TECHNIQUES;

FACE RECOGNITION;

EID: 84901191688     PISSN: 18669956     EISSN: 18669964     Source Type: Journal    
DOI: 10.1007/s12559-013-9224-1     Document Type: Article

References (48)

1. Clark A. Mindware: an introduction to the philosophy of cognitive science. New York: Oxford University Press; 2001.
2. Underwood G. Cognitive processes in eye guidance: algorithms for attention in image processing. Cogn Comput. 2009;1(1): 64-76.
3. Cambria E, Hussain A. Sentic album: content-, concept-, and context-based online personal photo management system. Cogn Comput. 2012;4(4): 477-96.
4. Nian R, Ji GR, Zhao WC, Feng C. Probabilistic 3D object recognition from 2D invariant view sequence based on similarity. Neurocomputing. 2007;70(4-6): 785-93.
5. Zhao W, Chellappa R, Phillips P, Rosenfeld A. Face recognition: a literature survey. ACM Comput Surv. 2003;35(4): 399-458.
6. Turk M, Pentland A. Eigenfaces for recognition. J Cogn Neurosci. 1991;3: 71-86.
7. Belhumeur PN, Hespanha JP, Kriegman DJ. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell. 1997;19(7): 711-20.
8. Cootes TF, Edwards GJ, Taylor CJ. Active appearance models. IEEE Trans Pattern Anal Mach Intell. 1998;23(6): 681-5.
9. Brunelli R, Poggio T. Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell. 1993;15(10): 1042-52.
10. Heisele B, Serre T, Poggio T. A component-based framework for face detection and identification. Int J Comput Vision. 2007;74(2): 167-81.
11. Zou J, Ji Q, Nagy G. A comparative study of local matching approach for face recognition. IEEE Trans Image Process. 2007;16(10): 2617-28.
12. Wiskott L, Fellous JM, Kuiger N, von der Malsburg C. Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell. 1997;19(7): 775-9.
13. Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. J Physiol. 1968;195(3): 215-43.
14. Olshausen BA, Field DJ. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 1996;381(13): 607-9.
15. Olshausen BA, Field DJ. Sparse coding with an over-complete basis set: a strategy employed by v1? Vis Res. 1997;37(23): 3311-25.
16. Lee H, Battle A, Raina R, Ng AY. Efficient sparse coding algorithms. Adv Neural Inf Process Syst. 2006;801-8.
17. Lewicki MS, Sejnowski TJ. Learning overcomplete representations. Neural Comput. 2000;12(2): 337-65.
18. Johnson JS, Olshausen BA. Timecourse of neural signatures of object recognition. J Vis. 2003;3(7): 499-512.
19. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2009;31(2): 210-27.
20. Huang JZ, Huang XL, Metaxas D. Simultaneous image transformation and sparse representation recovery. CVPR. 2008;1-8.
21. Wagner A, Wright J, Ganesh A, Zhou ZH, Ma Y. Towards a practical face recognition system: robust registration and illumination by sparse representation. CVPR. 2009;597-604.
22. Yang M, Zhang L. Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. ECCV. 2010;448-61.
23. Shan HH, Zhang LY, Cottrell GW. Recursive ICA. Adv Neural Inf Process Syst. 2006;1273-80.
24. Shan HH, Cottrell GW. Looking around the backyard helps to recognize faces and digits. CVPR. 2008;1-8.
25. Huang GB, Zhu Q, Siew CK. Extreme learning machine: theory and applications. Neurocomputing. 2006;70: 489-501.
26. Huang GB, Wang DH, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cybern. 2011;2: 107-22.
27. Huang GB, Chen L, Siew CK. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw. 2006;17(4): 879-92.
28. Huang GB, Chen L. Convex incremental extreme learning machine. Neurocomputing. 2007;70: 3056-62.
29. Huang GB, Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing. 2008;71: 3460-8.
30. Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multi-class classification. IEEE Trans Syst Man Cybern. 2012;42(2): 513-29.
31. Frénay B, Verleysen M. Using SVMs with randomized feature spaces: an extreme learning approach. In: Proceedings of the 18th European symposium on artificial neural networks (ESANN), Bruges, Belgium, 28-30 April 2010; 2010. p. 315-20.
32. Frénay B, Verleysen M. Parameter-insensitive kernel in extreme learning for non-linear support vector regression. Neurocomputing. 2011;74(16): 2526-31.
33. Huang GB, Li M, Chen L, Siew C-K CK. Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing. 2008;71: 576-83.
34. Liang N, Huang GB, Saratchandran P, Sundararajan N. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw. 2006;17(6): 1411-23.
35. Rong HJ, Huang GB, Sundararajan N, Saratchandran P. Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern. 2009;39(4): 1067-72.
36. Sun Y, Yuan Y, Wang G. An OS-ELM based distributed ensemble classification framework in P2P networks. Neurocomputing. 2011;74: 2438-43.
37. Rong H, Ong YS, Tan AH, Zhu Z. A fast pruned extreme learning machine for classification problem. Neurocomputing. 2008;72: 359-66.
38. Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A. OP-ELM: optimally-pruned extreme learning machine. IEEE Trans Neural Netw. 2010;21(1): 158-62.
39. Decherchi S, Gastaldo P, Zunino R, Cambria E, Redi J. Circular-ELM for the reduced-reference assessment of perceived image quality. Neurocomputing. 2013;102: 78-89.
40. van Heeswijk M, Miche Y, Lindh-Knuutila T, Hilbers PA, Honkela T, Oja E, Lendasse A. Adaptive ensemble models of extreme learning machines for time series prediction. Lect Notes Comput Sci. 2009;5769: 305-14.
41. van Heeswijk M, Miche Y, Oja E, Lendasse A. Gpu accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing. 2011;74: 2430-7.
42. Man ZH, Lee K, Wang DH, Cao ZW, Miao CY. A new robust training algorithm for a class of single-hidden layer feedforward neural networks. Neurocomputing. 2011;74: 2491-501.
43. Man ZH, Lee K, Wang DH, Cao ZW, Miao CY. A modified ELM algorithm for single-hidden layer feedforward neural networks with linear nodes. In: 2011 6th IEEE conference on industrial electronics and applications; 2011. p. 2524-9.
44. Cheng C, Tay WP, Huang GB. Extreme learning machines for intrusion detection. In: International joint conference on neural networks (IJCNN); 2012. p. 1-8.
45. Nian R, He B, Lendasse A. 3D object recognition based on a geometrical topology model and extreme learning machine. Neural Comput Appl. 2012;22(3-4): 427-33.
46. Wang GR, Zhao Y, Wang D. A protein secondary structure prediction framework based on the extreme learning machine. Neurocomputing. 2008;72: 262-8.
47. Bartlett PL. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory. 1998;44(2): 525-36.
48. Hansen LK, Salamon P. Neural network ensemble. IEEE Trans Pattern Anal Mach Intell. 1990;12(10): 993-1001.