Name
Abstract | ||
|---|---|---|
In this paper, we proposed a method for selecting edge- type features for iris recognition. The AdaBoost algorithm is used to select a filter bank from a pile of filter candi- dates. The decisions of the weak classifiers associated with the filter bank are linearly combined to form a strong clas- sifier. Real experiments have been conducted to assess the performance of the designed strong classifier. The results showed that the boosting algorithm can effectively improve the recognition accuracy at the cost of slightly increase the computation time. Keywords: Iris Recognition, Biometrics, Feature Extrac- tion, AdaBoost. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IIHMSP.2007.4457736 | IIH-MSP |
Keywords | Field | DocType |
adaboost algorithm,feature selection,strong classifier,recognition accuracy,iris recognition,computation time,feature extrac,filter candi,strong clas,filter bank,real experiment,ada,image recognition,feature extraction | Computer vision,Iris recognition,AdaBoost,Pattern recognition,Feature selection,Feature (computer vision),Computer science,Filter bank,Feature extraction,Feature (machine learning),Artificial intelligence,Boosting (machine learning) | Conference |
ISBN | Citations | PageRank |
0-7695-2994-1 | 4 | 0.39 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kan-Ru Chen | 1 | 4 | 0.39 |
| Chia-Te Chou | 2 | 30 | 3.27 |
| Sheng-Wen Shih | 3 | 415 | 46.87 |
| Wen-Shiung Chen | 4 | 93 | 12.36 |
| Duan-Yu Chen | 5 | 296 | 28.79 |