Name
Abstract | ||
|---|---|---|
Iris feature is widely used in recognition due to its uniqueness and reliability among other biometrics. Iris pattern is generally complex containing randomness and distinctive features such as furrows, freckles and crypts. It is still an uncertainty how useful each of these features will be when it comes to recognition. In this paper, we formulate the recognition problem in a semi-naïve Bayesian classification framework to maintain simplicity and robustness. A nonhierarchical structure is adopted to classify random feature sets in different ferns. Each feature set is a new binary representation and returns the probability that it belongs to any of the classes learned during training. The proposed method can synthesize multiple views of the iris features from a training image as they would appear under different scales and perspectives. The outcomes are then combined in a semi-naïve Bayesian manner. This new approach has been evaluated on CASIA iris database and benchmarked with Random Forest classifier and Hamming distance. The proposed method shows shorter processing time and lower equal error rate compared to other methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TAAI.2017.39 | 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI) |
Keywords | Field | DocType |
iris recognition,semi-naïve Bayesian,feature extraction,iris classification,biometrics | Iris recognition,Naive Bayes classifier,Pattern recognition,Computer science,Word error rate,Feature extraction,Hamming distance,Artificial intelligence,Biometrics,Random forest,Randomness | Conference |
ISBN | Citations | PageRank |
978-1-5386-4204-7 | 1 | 0.35 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tong-Yuen Chai | 1 | 5 | 1.14 |
| Bok-Min Goi | 2 | 498 | 62.02 |
| Yong Haur Tay | 3 | 225 | 20.14 |
| Wen-Jet Nyee | 4 | 1 | 0.35 |