Identifying Individuals using Robust Iris Recognition Techniques
| dc.contributor.author | ZEMOURI, Khadidja | |
| dc.contributor.author | Supervisor: ASSAS, Ouarda | |
| dc.contributor.author | Supervisor: BENOUIS, Mohamed | |
| dc.date.accessioned | 2023-05-22T13:16:14Z | |
| dc.date.available | 2023-05-22T13:16:14Z | |
| dc.date.issued | 2015-06-10 | |
| dc.description.abstract | The increasing need for information security has led to more attention being given to biometrics-based, automated personal identification. Among existing biometric approaches, the human iris is the most promising technique. This work is proposed the Iris recognition method using local binary pattern(LBP) and component analysis(PCA) with measure distance. In addition, we have introduced the multi biometrics system particularly multimodal system to improve the performance of the identification system. For the validation of this work, we use database CASIA VI. Experimental results show that the multimodal fusion scenario (Iris + Face) with the application of LBP and LDA on the iris and face respectively gives the best recognition rate of 98.33% using mean rule of fusion. | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/38551 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of M'sila | en_US |
| dc.subject | Iris recognition, face, Biometrics, PCA, LBP. LDA, Multibiometrics, fusion. | en_US |
| dc.title | Identifying Individuals using Robust Iris Recognition Techniques | en_US |
| dc.type | Thesis | en_US |