Name
Abstract | ||
|---|---|---|
Hand detection is the first step of any hand biometric recognition process, which determines the outcome of the following treatments. In this paper, we propose a robust method for hand detection without contact and without constraints on the capture environment for hand biometric applications. This method is based on a color based approach adopting a non-parametric modeling. Our contribution consists mainly in the choice of the most relevant color axes and the choice of the decision rules automatically using a process of the data-mining as a new philosophy of data processing. To improve the achieved results of skin detection and to determine the hand region in the image, a succession of post-processing was proposed. Our hand detection method was evaluated experimentally on a real database; the outcomes of this evaluation show promising results and demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/AICCSA.2013.6616500 | AICCSA |
Keywords | Field | DocType |
predictive models,data mining,skin,data processing,decision rules,prediction algorithms,databases | Decision rule,Data mining,Computer vision,Data processing,Pattern recognition,Image based,Hand region,Prediction algorithms,Artificial intelligence,Biometrics,Engineering | Conference |
ISSN | Citations | PageRank |
2161-5322 | 1 | 0.36 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salma Ben Jemaa | 1 | 14 | 1.91 |
| Mohamed Hammami | 2 | 181 | 30.54 |
| Hanêne Ben-Abdallah | 3 | 398 | 71.57 |