Name
Abstract | ||
|---|---|---|
Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. Features extracted using higher level wavelet decompositions are shown to yield better clustering and higher success rate in recognition. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ARTCom.2009.14 | ARTCom |
Keywords | Field | DocType |
iris recognition,iris feature extraction,feature extraction,poor quality image,iris image,higher success rate,features subset,iris recognition system,higher level wavelet decomposition,recognition module,personal identification,feature extraction module,biometric identification,image recognition,iris,wavelet transforms,wavelet transform,euclidean distance | Computer vision,Iris recognition,Pattern recognition,Computer science,Euclidean distance,Feature extraction,Preprocessor,Artificial intelligence,Biometrics,Cluster analysis,Wavelet transform,Wavelet | Conference |
Citations | PageRank | References |
2 | 0.44 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chandrashekar M. Patil | 1 | 3 | 1.14 |
| Sudarshan Patil Kulkarni | 2 | 2 | 0.44 |