Eye detection based on rectangle features and pixel-pattern-based texture features

Abstract

Eye detection can be used in intelligent human-computer interfaces, driver drowsiness detection, security, and biology systems. In this paper a new method for eye detection based on rectangle features and pixel-pattern-based texture feature (PPBTF) is proposed. First, Adaboost cascade classifier by rectangle features is constructed to do rough eye detection in a front facial image. Second, the result image patches are cropped and scaled to 24×12 to compute the features of PPBTF, then, put these features into an Adaboost and SVM classifier for an accurate detection. Some eye open front facial images from FERET Database are chosen to do the experiments, and the results show that the approach is real time and is effective for eye detection.