Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A robust eyelash detection based on iris focus assessment
Pattern Recognition Letters
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Investigating useful and distinguishing features around the eyelash region
AIPR '08 Proceedings of the 2008 37th IEEE Applied Imagery Pattern Recognition Workshop
Tear-duct detector for identifying left versus right iris images
AIPR '08 Proceedings of the 2008 37th IEEE Applied Imagery Pattern Recognition Workshop
Periocular biometrics in the visible spectrum: a feasibility study
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Personal identification using periocular skin texture
Proceedings of the 2010 ACM Symposium on Applied Computing
On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Genetic-Based Type II Feature Extraction for Periocular Biometric Recognition: Less is More
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Circuits and Systems for Video Technology
Image security and biometrics: a review
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
| Hi-index | 0.00 |
The periocular region is the part of the face immediately surrounding the eye, and researchers have recently begun to investigate how to use the periocular region for recognition. Understanding how humans recognize faces helped computer vision researchers develop algorithms for face recognition. Likewise, understanding how humans analyze periocular images could benefit researchers developing algorithms for periocular recognition. We conducted two experiments to determine how humans analyze periocular images. In these experiments, we presented pairs of images and asked volunteers to determine whether the two images showed eyes from the same subject or from different subjects. In the first experiment, subjects were paired randomly to create different-subject queries. Our volunteers correctly determined the relationship between the two images in 92% of the queries. In the second experiment, we considered multiple factors in forming different-subject pairs; queries were formed from pairs of subjects with the same gender and race, and with similar eye color, makeup, eyelash length, and eye occlusion. In addition, we limited the amount of time volunteers could view a query pair. On this harder experiment, the correct verification rate was 79%. We asked volunteers to describe what features in the images were helpful to them in making their decisions. In both experiments, eyelashes were reported to be the most helpful feature.