Name
Abstract | ||
|---|---|---|
Face recognition systems robust to major occlusions have wide applications ranging from consumer products with biometric features to surveillance and law enforcement applications. In unconstrained scenarios, faces are often subject to occlusions, apart from common variations such as pose, illumination, scale, orientation and so on. In this paper we propose a novel Bayesian oriented occlusion model inspired by psychophysical mechanisms to recognize faces prone to occlusions amidst other common variations. We have discovered and modeled similarity maps that exist in facial domains by means of Bayesian Networks. The proposed model is capable of efficiently learning and exploiting these maps from the facial domain. Hence it can tackle the occlusion uncertainty reasonably well. Improved recognition rates over state of the art techniques have been observed. |
Year | Venue | Keywords |
|---|---|---|
2011 | CAIP (1) | occluded face,occlusion uncertainty,improved recognition rate,common variation,art technique,major occlusion,face recognition system,bayesian networks,occlusion model,facial domain,bayesian occlusion model,parameter estimation,face recognition,similarity |
Field | DocType | Volume |
Computer vision,Facial recognition system,Occlusion,Pattern recognition,Computer science,Bayesian network,Ranging,Artificial intelligence,Estimation theory,Biometrics,Law enforcement,Bayesian probability | Conference | 6854 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.39 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim Venkat | 1 | 70 | 14.37 |
| Ahamad Tajudin Khader | 2 | 683 | 40.71 |
| K. G. Subramanian | 3 | 339 | 59.27 |
| Philippe De Wilde | 4 | 192 | 23.86 |