Fast eye localization without a face model using inner product detectors

Many applications in the area of human-machine interaction require accurate and fast facial landmarks localization. Non-invasive techniques are even more essential when the goal is social inclusion. In this work, we propose a novel approach for the detection of landmarks on faces. We introduce a new detector, the Inner Product Detector (IPD), based on correlation filters. The main advantages of the proposed method are the tolerance to small variance on the desired patterns, a low computational cost, and the generalization for other features. We evaluate our method using the BioID dataset and compare it to the state-of-the-art techniques.