Name
Abstract | ||
|---|---|---|
Eye localization is a key step in many face analysis related applications. In this paper, we present a novel eye localization method based on a group of trained filters called correlation filter bank (CFB). We formulate the eye localization problem as an optimization problem with a well-defined cost function based on CFB. The CFB is trained with an EM-like adaptive clustering approach. The trained filter bank includes several discriminative filter templates, each of them suits to a different face condition from the others, thus can provide accurate eye localization ability for variable poses, appearances and illuminations. Simulation comparisons with cascade classifier-based method [1], traditional single correlation filter based methods [2][3] and pictorial structure model based method [4] demonstrates the superiority of the proposed method both in detection ratio and localization accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICME.2014.6890249 | ICME |
Keywords | Field | DocType |
regression,optimisation,pattern clustering,face recognition,face analysis related applications,adaptive clustering,filter bank,em-like adaptive clustering approach,optimization problem,channel bank filters,eye localization,pictorial structure model,correlation filter,discriminative filter templates,correlation filter bank,cost function,eye localization method,cascade classifier-based method,cfb,optimization,correlation,accuracy,face,testing | Computer vision,Correlation filter,Regression,Pattern recognition,Computer science,Cascading classifiers,Filter bank,Correlation,Artificial intelligence,Cluster analysis,Discriminative model,Optimization problem | Conference |
ISSN | Citations | PageRank |
1945-7871 | 1 | 0.36 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiming Ge | 1 | 106 | 24.60 |
| Rui Yang | 2 | 9 | 2.20 |
| Hui Wen | 3 | 8 | 4.31 |
| Shuixian Chen | 4 | 6 | 1.81 |
| Sun Limin | 5 | 467 | 65.09 |