Name
Abstract | ||
|---|---|---|
Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2228520 | Proceedings of SPIE |
Keywords | Field | DocType |
Age recognition,Gender recognition,Digital signage system,ASM,LBP,Histogram matching | Categorization,Computer vision,Broadcasting,Normalization (statistics),Recognition system,Computer science,Local binary patterns,Digital signage,Histogram matching,Artificial intelligence | Conference |
Volume | ISSN | Citations |
9875 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sang-Heon Lee | 1 | 105 | 22.48 |
| Myoung-Kyu Sohn | 2 | 33 | 7.17 |
| Hyunduk Kim | 3 | 49 | 10.91 |