Accurate Eye Center Location through Invariant Isocentric Patterns

• Published in: IEEE transactions on pattern analysis and machine intelligence 2012-09, Vol.34 (9), p.1785-1798
• Main Authors: Valenti, R., Gevers, T.
• Format: Article
• Language: English
• Subjects: Algorithms; Analytical models; Applied sciences; Artificial Intelligence; Computer science; control theory; systems; Databases, Factual; Exact sciences and technology; Eye center location; Eye Movements - physiology; Eyes; Face; facial features detection; Feature extraction; Gain; Humans; Illumination; Image edge detection; Image Processing, Computer-Assisted - methods; Image resolution; Invariance; Invariants; Iris - anatomy & histology; Iris - physiology; isophotes; Lighting; Microwave integrated circuits; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Position (location); Product introduction; Pupil - physiology; Vectors
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2011.251

Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using commercial eye-gaze trackers, but additional constraints and expensive hardware make these existing solutions unattractive and impossible to use on standard (i.e., visible wavelength), low-resolution images of eyes. Systems based solely on appearance are proposed in the literature, but their accuracy does not allow us to accurately locate and distinguish eye centers movements in these low-resolution settings. Our aim is to bridge this gap by locating the center of the eye within the area of the pupil on low-resolution images taken from a webcam or a similar device. The proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve in-plane rotational invariance, and to keep low-computational costs. To further gain scale invariance, the approach is applied to a scale space pyramid. In this paper, we extensively test our approach for its robustness to changes in illumination, head pose, scale, occlusion, and eye rotation. We demonstrate that our system can achieve a significant improvement in accuracy over state-of-the-art techniques for eye center location in standard low-resolution imagery.