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Accuracy 0.5: 90.56
Global Accuracy: 79.41
Valid images (%): 99.43
Authors: Arantxa Villanueva, Victoria Ponz, Laura Sesma‐Sanchez, Mikel Ariz, Sonia Porta, and Rafael Cabeza
Filiation: Public University of Navarre
Link to group/authors: http://gi4e.unavarra.es/
Algorithm name: GI4E iris detector
Reference if published: Arantxa Villanueva, Victoria Ponz, Laura Sesma‐Sanchez, Mikel Ariz, Sonia Porta, and Rafael Cabeza. “Hybrid method based on topography for robust detection of iris center and eye corners”, ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP). 9, 4 (2013)
Development platform: MatLab
Execution time (indicating execution platform):Not Specified
Memory requisites: Not Specified
Training requisites: Not Specified
Link to source: Not Specified
Abstract (5000 chars): A multistage procedure to detect eye features is presented. Multiresolution and topographic classification are used to detect the iris center. The eye corner is calculated combining valley detection and eyelid curve extraction. The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images. The results show that the suggested algorithm is robust and accurate. Regarding the iris center our method obtains the best average behavior for the BioID database compared to other available algorithms. Additional contributions are that our algorithm functions in real time and does not require complex post processing stages