TY - GEN
T1 - Gaze estimation for off-Angle iris recognition based on the biometric eye model
AU - Karakaya, Mahmut
AU - Barstow, Del
AU - Santos-Villalobos, Hector
AU - Thompson, Joseph
AU - Bolme, David
AU - Boehnen, Christopher
PY - 2013
Y1 - 2013
N2 - Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-Angle iris recognition framework based on the ORNL biometric eye model. Gaze estimation is an important prerequisite step to correct an off-Angle iris images. To achieve the accurate frontal reconstruction of an off-Angle iris image, we first need to estimate the eye gaze direction from elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.
AB - Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-Angle iris recognition framework based on the ORNL biometric eye model. Gaze estimation is an important prerequisite step to correct an off-Angle iris images. To achieve the accurate frontal reconstruction of an off-Angle iris image, we first need to estimate the eye gaze direction from elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.
KW - Biometric eye model
KW - Elliptical iris boundary
KW - Gaze estimation
KW - Iris recognition
KW - Off-Angle iris
UR - http://www.scopus.com/inward/record.url?scp=84881038636&partnerID=8YFLogxK
U2 - 10.1117/12.2018614
DO - 10.1117/12.2018614
M3 - Conference contribution
AN - SCOPUS:84881038636
SN - 9780819495037
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Biometric and Surveillance Technology for Human and Activity Identification X
T2 - Biometric and Surveillance Technology for Human and Activity Identification X
Y2 - 2 May 2013 through 2 May 2013
ER -