Accurate eye localization in low and standard definition content

In this paper we address the problem of eye localization in low and standard definition content, such as webcam-generated and TV images. We present a probabilistic eye localization method based on well-known multiscale local binary patterns (LBPs), which provide a simple and powerful spatial description of texture, and are robust to the noise typical to low and standard definition content. Our primary contribution is in the proposed method of combining the LBPs that is targeted towards achieving spatial accuracy under mentioned conditions. Evaluation performed on a standard dataset of webcam-quality images shows that our approach has superior performance with respect to the state of the art, while having a reasonable complexity and a low memory footprint.