TY - GEN
T1 - Iris segmentation using a statistical approach
AU - Zamudio-Fuentes, Luis M.
AU - García-Vázquez, Mireya S.
AU - Ramírez-Acosta, Alejandro A.
PY - 2010
Y1 - 2010
N2 - Eyelashes and reflections occluding the iris region are noise factors that degrade the performance of iris recognition. If these factors are not eliminated in iris segmentation phase, they are incorrectly considered as the iris region. Thus, produce false iris pattern information which decreases the recognition rate. In this paper a statistical approach is used to improve iris segmentation phase eliminating this noise from none constrain images, which is composed in three parts, finding the pupil and limbus boundary, reflection detection and eyelash detection. First an edge map is calculated using canny filter then the Circular Hough Transform is used to improve circle parameter finding. An intensity variation analysis is use to recognize a strong reflection. Eyelashes are classified in two categories, separable and multiple. Intensity variances are used to detect multiple eyelashes and an edge detector to localize separable eyelashes. The results show that statistics are useful to decide when is necessary applied the eyelash detector.
AB - Eyelashes and reflections occluding the iris region are noise factors that degrade the performance of iris recognition. If these factors are not eliminated in iris segmentation phase, they are incorrectly considered as the iris region. Thus, produce false iris pattern information which decreases the recognition rate. In this paper a statistical approach is used to improve iris segmentation phase eliminating this noise from none constrain images, which is composed in three parts, finding the pupil and limbus boundary, reflection detection and eyelash detection. First an edge map is calculated using canny filter then the Circular Hough Transform is used to improve circle parameter finding. An intensity variation analysis is use to recognize a strong reflection. Eyelashes are classified in two categories, separable and multiple. Intensity variances are used to detect multiple eyelashes and an edge detector to localize separable eyelashes. The results show that statistics are useful to decide when is necessary applied the eyelash detector.
KW - Iris recognition
KW - biometric
KW - eyelash detector
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=78751482950&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15992-3_18
DO - 10.1007/978-3-642-15992-3_18
M3 - Contribución a la conferencia
SN - 3642159915
SN - 9783642159916
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 170
BT - Advances in Pattern Recognition - Second Mexican Conference on Pattern Recognition, MCPR 2010, Proceedings
T2 - Mexican Conference on Pattern Recognition 2010, MCPR 2010
Y2 - 27 September 2010 through 29 September 2010
ER -