Lattice computing extension of the fam neural classifier for human facial expression recognition

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 10, 2013, Pages 1526-1538

  Kaburlasos, Vassilis G ^a   Papadakis, Stelios E ^b,c   Papakostas, George A ^a  

^a Human Machines Interaction (HMI) Laboratory   (Greece)

^b TECHNOLOGICAL EDUCATIONAL INSTITUTE OF CRETE   (Greece)

^c TEI of Kavala   (Greece)

Author keywords

Fuzzy ARTMAP (FAM); fuzzy lattice reasoning (FLR); inclusion measure; intervals' number (IN; lattice computing (LC) paradigm

Indexed keywords

FUZZY ARTMAP; FUZZY LATTICE; HUMAN FACIAL EXPRESSIONS; HUMAN MACHINE INTERACTION; INCLUSION MEASURE; LATTICE COMPUTING (LC) PARADIGM; PARAMETER OPTIMIZATION; REASONING TECHNIQUES;

FEATURE EXTRACTION; FUZZY NEURAL NETWORKS; GESTURE RECOGNITION;

HUMAN COMPUTER INTERACTION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; FACIAL EXPRESSION; FUZZY LOGIC; HUMAN; LEARNING; TIME;

ALGORITHMS; COMPUTER SIMULATION; FACIAL EXPRESSION; FUZZY LOGIC; HUMANS; LEARNING; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; TIME FACTORS;

EID: 84885105401     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2012.2237038     Document Type: Article

References (49)

1. D. Aarno and D. Kragic, Motion intention recognition in robot assisted applications Robot. Auto. Syst., vol. 56, no. 8, pp. 692-705, 2008.
2. J. Andreu and P. Angelov, Real-time human activity recognition from wireless sensors using evolving fuzzy systems in Proc. IEEE Int. Conf. Fuzzy Syst., Jul. 2010, pp. 1-8.
3. G. Birkhoff, Lattice Theory, vol. 25. Providence, RI: AMS, 1967.
4. G. A. Carpenter and S. Grossberg, A massively parallel architecture for a self-organizing neural pattern recognition machine Comput. Vis., Graph., Image Process., vol. 37, no. 1, pp. 54-115, 1987.
5. G. A. Carpenter, S. Grossberg, and J. H. Reynolds, A fuzzy ARTMAP nonparametric probability estimator for nonstationary pattern recognition problems IEEE Trans. Neural Netw., vol. 6, no. 6, pp. 1330-1336, Nov. 1995.
6. G. A. Carpenter, S. Grossberg, and D. B. Rosen, Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system Neural Netw., vol. 4, no. 6, pp. 759-771, 1991.
7. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 698-713, Sep. 1992.
8. R. Chattopadhyay, S. Chakraborty, V. N. Balasubramanian, and S. Panchanathan, Optimization-based domain adaptation towards personadaptive classification models in Proc. 10th Int. Conf. Mach. Learn. Appl. Workshops, Honolulu, HI, Dec. 2011, pp. 476-483.
9. X.-w. Chen and T. Huang, Facial expression recognition: A clustering-based approach Pattern Recognit. Lett., vol. 24, nos. 9-10, pp. 1295-1302, 2003.
10. F. Courtemanche, E. Aïmeur, A. Dufresne, M. Najjar, and F. Mpondo, Activity recognition using eye-gaze movements and traditional interactions Interact. Comput., vol. 23, no. 3, pp. 202-213, 2011.
11. F. Dornaika, E. Lazkano, and B. Sierra, Improving dynamic facial expression recognition with feature subset selection Pattern Recognit. Lett., vol. 32, no. 5, pp. 740-748, 2011.
12. R. Elwell and R. Polikar, Incremental learning of concept drift in nonstationary environments IEEE Trans. Neural Netw., vol. 22, no. 10, pp. 1517-1531, Oct. 2011.
13. T. Fawcett, An introduction to ROC analysis Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, 2006.
14. M. Graña, A brief review of lattice computing in Proc. World Congr. Comput. Intell. FUZZ-iEEE Program, Hong Kong, China, Jun. 2008, pp. 1777-1781.
15. M. Graña, Special issue on: Lattice computing and natural computing Neurocomputing, vol. 72, nos. 10-12, pp. 2065-2066, 2009.
16. M. Graña, D. Chyzhyk, M. García-sebastián, and C. Hernández, Lattice independent component analysis for functional magnetic resonance imaging Inf. Sci., vol. 181, no. 10, pp. 1910-1928, 2011.
17. W. Gu, C. Xiang, Y. V. Venkatesh, D. Huang, and H. Lin, Facial expression recognition using radial encoding of local Gabor features and classifier synthesis Pattern Recognit., vol. 45, no. 1, pp. 80-91, 2012.
18. Z. Hammal and M. Kunz, Pain monitoring: A dynamic and contextsensitive system Pattern Recogn., vol. 45, no. 4, pp. 1265-1280, 2012.
19. M. Ilbeygi and H. Shah-hosseini, A novel fuzzy facial expression recognition system based on facial feature extraction from color face images Eng. Appl. Artif. Intell., vol. 25, no. 1, pp. 130-146, 2012.
20. V. G. Kaburlasos, Adaptive resonance theory with supervised learning and large database applications Ph.D. dissertation, Dept. Electr. Eng., Univ. Nevada, Reno, 1992.
21. V. G. Kaburlasos, FINs: Lattice theoretic tools for improving prediction of sugar production from populations of measurements IEEE Trans. Syst., Man Cybern., B, vol. 34, no. 2, pp. 1017-1030, Apr. 2004.
22. V. G. Kaburlasos, Towards a Unified Modeling and Knowledge-representation Based on Lattice Theory (Studies in Computational Intelligence). Heidelberg, Germany: Springer-Verlag, 2006.
23. V. G. Kaburlasos, Special issue on: Information engineering applications based on lattices Inf. Sci., vol. 181, no. 10, pp. 1771-1773, 2011.
24. V. G. Kaburlasos and A. Kehagias, Novel fuzzy inference system (FIS) analysis and design based on lattice theory IEEE Trans. Fuzzy Syst., vol. 15, no. 2, pp. 243-260, Apr. 2007.
25. V. G. Kaburlasos and T. Pachidis, A lattice-computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application Inf. Fusion, to be published.
26. V. G. Kaburlasos and S. E. Papadakis, Granular self-organizing map (grSOM) for structure identification Neural Netw., vol. 19, no. 5, pp. 623-643, 2006.
27. V. G. Kaburlasos and S. E. Papadakis, A granular extension of the fuzzy-aRTMAP (FAM) neural classifier based on fuzzy lattice reasoning (FLR) Neurocomputing, vol. 72, nos. 10-12, pp. 2067-2078, 2009.
28. V. G. Kaburlasos and V. Petridis, Fuzzy lattice neurocomputing (FLN) models Neural Netw., vol. 13, no. 10, pp. 1145-1169, 2000.
29. V. G. Kaburlasos, I. N. Athanasiadis, and P. A. Mitkas, Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation Int. J. Approx. Reason., vol. 45, no. 1, pp. 152-188, 2007.
30. V. G. Kaburlasos, S. E. Papadakis, and A. Amanatiadis, Binary image 2D shape learning and recognition based on lattice-computing (LC) techniques J. Math. Imag. Vis., vol. 42, nos. 2-3, pp. 118-133, 2012.
31. A. Kehagias, Some remarks on the lattice of fuzzy intervals Inf. Sci., vol. 181, no. 10, pp. 1863-1873, 2011.
32. S. M. Lajevardi and Z. M. Hussain, Higher order orthogonal moments for invariant facial expression recognition Digital Signal Process., vol. 20, no. 6, pp. 1771-1779, 2010.
33. O. Langner, R. Dotsch, G. Bijlstra, D. H. J. Wigboldus, S. T. Hawk, and A. van Knippenberg, Presentation and validation of the Radboud faces database Cognit. Emotion, vol. 24, no. 8, pp. 1377-1388, 2010.
34. M. J. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, Coding facial expressions with Gabor wavelets in Proc. 3rd Int. Conf. Autom. Face Gesture Recognit., Nara, Japan, Apr. 1998, pp. 200-205.
35. L. Ma and K. Khorasani, Facial expression recognition using constructive feedforward neural networks IEEE Trans. Syst., Man Cybern., B, vol. 34, no. 3, pp. 1588-1595, Jun. 2004.
36. J. M. Mendel, Type-2 fuzzy sets and systems: An overview IEEE Comput. Intell. Mag., vol. 2, no. 1, pp. 20-29, Feb. 2007.
37. L. L. Minku and X. Yao, DDD: A new ensemble approach for dealing with concept drift IEEE Trans. Knowl. Data Eng., vol. 24, no. 4, pp. 619-633, Apr. 2012.
38. T. D. Nguyen and S. Ranganath, Facial expressions in American sign language: Tracking and recognition Pattern Recognit., vol. 45, no. 5, pp. 1877-1891, 2012.
39. M. Pantic, Machine understanding of human behaviour in Proc. 16th Int. Conf. Knowl.-based Intell. Inf. Eng. Syst., San Sebastian, Spain, Sep. 2012, pp. 13-24.
40. M. Pantic, A. Nijholt, A. Pentland, and T. S. Huang, Human-centred intelligent human-computer interaction (HCI2): How far are we from attaining it? Int. J. Auto. Adaptive Commun. Syst., vol. 1, no. 2, pp. 168-187, 2008.
41. S. E. Papadakis and V. G. Kaburlasos, Piecewise-Linear approximation of non-Linear models based on probabilistically/possibilistically interpreted intervals? numbers (INs) Inf. Sci., vol. 180, no. 24, pp. 5060-5076, 2010
42. S. E. Papadakis, V. G. Kaburlasos, and G. A. Papakostas, Fuzzy lattice reasoning (FLR) classifier for human facial expression recognition in Proc. 10th Int. Conf. Uncertainty Model. Knowl. Eng. Decision Making, Istanbul, Turkey, Aug. 2012, pp. 633-638.
43. G. A. Papakostas, E. G. Karakasis, and D. E. Koulouriotis, Novel moment invariants for improved classification performance in computer vision applications Pattern Recognit., vol. 43, no. 1, pp. 58-68, 2010.
44. G. A. Papakostas, D. E. Koulouriotis, and E. G. Karakasis, A unified methodology for efficient computation of discrete orthogonal image moments Inf. Sci., vol. 179, no. 20, pp. 3619-3633, 2009.
45. F. Y. Shih, C.-f. Chuang, and P. S. P. Wang, Performance comparisons of facial expression recognition in JAFFE database Int. J. Pattern Recognit. Artif. Intell., vol. 22, no. 3, pp. 445-459, 2008.
46. P. Sussner and E. L. Esmi, Morphological perceptrons with competitive learning: Lattice-theoretical framework and constructive learning algorithm Inf. Sci., vol. 181, no. 10, pp. 1929-1950, 2011.
47. M. F. Valstar, M. Mehu, B. Jiang, M. Pantic, and K. Scherer, Metaanalysis of the first facial expression recognition challenge IEEE Trans. Syst., Man Cybern., B, vol. 42, no. 4, pp. 966-979, Aug. 2012.
48. P. Viola and M. J. Jones, Robust real-time face detection Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, 2004.
49. C. Zhan, W. Li, P. Ogunbona, and F. Safaei, A real-time facial expression recognition system for online games Int. J. Comput. Games Technol., vol. 2008, pp. 542918-1-542918-7, Jan. 2008.