| Full text | |
| Author(s): |
Angelon, Marcus de Assis
;
Pedrini, Helio
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC); v. N/A, p. 6-pg., 2016-01-01. |
| Abstract | |
In recent years, we can observe an increasing use of biometric technology in our daily lives. Face recognition has several advantages over other biometric modalities, since that it is natural, nonintrusive, and it is a task that humans perform routinely and effortlessly. Following a recent trend in this research field, this paper focuses on a part-based face recognition, exploring and evaluating specific descriptions I classifications for each facial part. Experimental results obtained in three public datasets (AR Face, MUCT and XM2VTS), assessing 15 approaches in each facial part and two score fusion strategies, show that features and classifiers specific for facial part can improve the accuracy of biometric systems, achieving error rates close to zero in some cases, including scenarios where the false acceptance cases are critical. (AU) | |
| FAPESP's process: | 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications |
| Grantee: | Luis Gustavo Nonato |
| Support Opportunities: | Research Projects - Thematic Grants |