ORB Features and Isophotes Curvature Information for Eye Center Accurate Localization

Abstract

Pupil center recognition and location is an essential branch of ergonomics. It can be applied to emotion analysis and attention judgment. How to get the position of the pupil center from eye photos is the core of this field. Previous studies provided a helpful method, using scale-invariant feature transform (SIFT) to extract relevant features and combine them with the K-Nearest Neighbor (KNN) classifier. However, this method’s accuracy is not satisfying, and under some conditions, it will be position drift and other problems. We put forward a new idea to solve it by using Oriented FAST and Rotated BRIEF (ORB) features and Random Forest (RF) classifies. It is proved by experiment that our method improves the robustness of localization and the use of isophotes yields low computational cost, allowing for real-time processing. Meanwhile, we found that the ORB and RF are nearly as good, yielding an accuracy of 92.88% (BioID database).