Notice: Undefined index: linkPowrot in C:\wwwroot\wwwroot\publikacje\publikacje.php on line 1275
Publikacje
Pomoc (F2)
[51030] Artykuł:

Face Recognition for Movie Character and Actor Discrimination Based on Similarity Scores

Czasopismo: Proceedings of the 2016 International Conference on Computational Science and Computational Intelligence (CSCI 2016)   Strony: 1333-1338
ISBN:  978-1-5090-5510-4
Wydawca:  IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Opublikowano: Grudzień 2016
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Procent
udziału
Liczba
punktów
Remigiusz Baran orcid logoWEAiIKatedra Informatyki, Elektroniki i Elektrotechniki *457.50  
Filip Rudziński orcid logoWEAiIKatedra Informatyki, Elektroniki i Elektrotechniki *457.50  
Andrzej Zeja10.00  

Grupa MNiSW:  Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science)
Punkty MNiSW: 15
Klasyfikacja Web of Science: Proceedings Paper


Pełny tekstPełny tekst     DOI LogoDOI     Web of Science Logo Web of Science    
Keywords:

face recognition  video indexing  data enrichment  content discovery platform  similarity measures 



Abstract:

A novel face recognition approach dedicated to discriminate between movie characters and actors is presented in the paper. In presented approach, faces are categorized according to similarities determined with regard to their ORB feature descriptors. Constituent procedures, including face normalization, descriptors computation as well as feature matching and similarity discovering are described accurately. Results of their examination are then also reported in details. An exemplary application of the presented approach within the IMCOP platform is presented as well.



B   I   B   L   I   O   G   R   A   F   I   A
1. R. Baran, and A. Zeja, “The IMCOP System for Data Enrichment and Content Discovery and Delivery”, 2015 Intern. Conf. on Computational Science and Computational Intelligence, Las Vegas, 2015, pp. 143-146, doi: 10.1109/CSCI.2015.137,
2. S. Mitchell, M. B. Blake, D. Cunningham and S. Gopalan, "A SOA-Driven Content Discovery and Retrieval Platform," 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services, Washington, DC, 2008, pp. 424-427, doi: 10.1109/CECandEEE.2008.145,
3. https://spring.io/understanding/REST,
4. R. Baran, T. Ruść, M. Rychlik, “A Smart Camera for Traffic Surveillance”. Communications in Computer and Information Science, Multimedia Communications, Services and Security, vol. 429, pp 1-15, Springer, 2014, doi: 10.1007/978-3-319-07569-3_1,
5. P. Slusarczyk, and R. Baran, “Piecewise-linear Subband Coding Scheme for Fast Image De-composition”, Multimedia Tools and Applications, vol. 75, issue 17, pp. 10649-10666, Springer US (2016), doi: 10.1007/s11042-014-2173-1,
6. P. Romaniak, L. Janowski, M. Leszczuk, and Z. Papir, “Perceptual Quality Assessment for H.264/AVC Compression”. In: Proc. of Consumer Communications and Networking Conference (CCNC), pp 597-602 (2012), http://dx.doi.org/10.1109/CCNC.2012.6181021,
7. https://imcop.pl/,
8. http://www.viaccess-orca.com/content-discovery-platform.html,
9. M. B. Gorzałczany, J. Piekoszewski, and F. Rudziński, "Generalized tree-like self-organizing neural networks with dynamically defined neighborhood for cluster analysis", Lecture Notes in Computer Science, vol. 8468, pp. 725-737, 2014.
10. M. B. Gorzałczany and F. Rudziński, "Cluster analysis via dynamic self-organizing neural networks", Lecture Notes in Computer Science, vol. 4029, pp. 593-602, 2006.
11. M. B. Gorzałczany and F. Rudziński, "Handling fuzzy systems' accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods - selected problems", Bull. of the Polish Academy of Sci. Tech. Sci., vol. 63, no. 3, pp. 791-798, 2015.
12. F. Rudziński, "A multi-objective genetic optimization of interpretability oriented fuzzy rule-based classifiers", Appl. Soft Comput., vol. 38, pp. 118-133, 2016