Face recognition by artificial vision systems: A cognitive perspective

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 22, Issue 5, 2008, Pages 899-913

  Raducanu, Bogdan ^a   Vitrià, Jordi ^a,b  

^a Barcelona Perception Computing Lab   (Spain)

^b UNIVERSITY OF BARCELONA   (Spain)

Author keywords

Cognitive development; Face recognition; Online learning; Pattern classification

Indexed keywords

ARSENIC COMPOUNDS; BOOLEAN FUNCTIONS; COMPUTER NETWORKS; DATA STORAGE EQUIPMENT; FACE RECOGNITION; INTERNET; LEARNING SYSTEMS; ONLINE SYSTEMS; TREES (MATHEMATICS); VISUAL COMMUNICATION;

COGNITIVE DEVELOPMENT; LEARNING STRATEGIES; ONLINE LEARNING; PATTERN CLASSIFICATION;

LEARNING ALGORITHMS;

EID: 51449093544     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001408006545     Document Type: Article

References (18)

1. M. Artač, M. Jogan and A. Leonardis, Incremental PCA for on-line visual learning and recognition, Proc. 16th Int. Conf. Pattern Recognition, Québec, Canada 3 (2002) 781-784
2. R. C. Atkinson and R. M. Schiffrin, Human memory: a proposed system and its control processes, The Psychology of Learning and Motivation: Advances in Research and Theory, eds. K. W. Spence and J. T. Spence, Vol. 2 (Academic Press, London, 1968).
3. R. A. Brooks, Intelligence without reason, Proc. Int. Joint Conf. Artificial Intelligence (IJCAI) Sydney, Australia (1991), pp. 569-595.
4. G. Cauwenberghs and T. Poggio, Incremental and decremental support vector machine learning, Adv. Neural Information Processing Systems (NIPS*2000), Vol. 13 (MIT Press, Cambridge, MA, 2001).
5. S. Chandrasekaran, B. S. Manjumath, Y. F. Wang, J. Winkler and H. Zhang, An eigenspace update algorithm for image analysis, Graph. Mod. Imag. Process. 59 (1997) 321-332.
6. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd edn. (John Wiley and Sons, NY, USA, 2001).
7. T.-K. Kim, S.-F. Wong, B. Stenger, J. Kittler and R. Cipolla, Incremental linear discriminant analysis using sufficient spanning set approximations, Proc. IEEE Conf. Comput. Vision Patt. Recog. (CVPR), Minneapolis, USA, 2007.
8. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd edn. (Academic Press, Boston, USA, 1990).
9. P. Hall, D. Marshall and R. Martin, Incremental eigenanalysis for classification, Proc. British Machine Vision Conf., Southampton, UK, 1 (1998) 286-295.
10. G. A. Miller, The magical number seven, plus or minus two: some limits on our capacity for processing information, Psychol. Rev. 63 (1956) 81-97.
11. S. Pang, S. Ozawa and N. Kasabov, Chunk incremental LDA computing on data streams, Advances in Neural Networks, eds. Wang et al., Lecture Notes in Computer Science, Vol. 3497 (Springer-Verlag, Berlin, 2005), pp. 51-56.
12. B. Raducanu and J. Vitrià, Incremental subspace learning for cognitive visual processes, Advances in Brain, Vision and Artficial Intelligence, eds. F. Mele et al., Lecture Notes in Computer Science, Vol. 4729 (Springer-Verlag, Berlin, 2007), pp. 214-223.
13. D. Skočaj, M. Uray, A. Leonardis and H. Bischof, Why to combine reconstructive and discriminative information for incremental subspace learning, Proc. Computer Vision Winter Workshop, eds. Chum et al., Telč, Czech Republic (2006).
14. D. M. J. Tax and P. Laskov, Online SVM learning: from classification to data description and back, Proc. IEEE Workshop on Neural Networks for Signal Processing, Toulouse, France (September 2003).
15. A. Tsymbal, The problem of concept drift: definitions and related work, Technical Report TCD-CS-2004-15, Computer Science Department, Trinity College Dublin (2004).
16. G. V. Veres, M. S. Nixon and J. N. Carter, Is enough enough? What is sufficiency in biometric data, Image Analysis and Recognition, eds. A. Campilho and M. Karmel, Lecture Notes in Computer Science, Vol. 4142 (Springer-Verlag, Berlin, 2006), pp. 262-273.
17. P. Viola and M. J. Jones, Robust real-time face detection, Int. J. Comput. Vis. 57 (2004) 137-154.
18. J. Weng, Y. Zhang and W.-S. Hwang, Candid covariance-free incremental principal component analysis, IEEE Trans. Patt. Anal. Mach. Intell. 25 (2003) 1034-1040.