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In this work, we propose a novel approach to detect and track, in videoconference sequences, six landmarks on eyes: the four corners and the pupils. Detection is based on the Inner Product Detector (IPD), and tracking on the Lucas-Kanade (LK) technique. The novelty of our method consists in the integration between detection and tracking, the evaluation of the temporal consistency to decrease the false positive rates, and the use of geometrical constraints to infer the position of missing points. In our experiments, we use five high definition video sequences with four subjects, different types of background, fast movements, blurring and occlusion. The obtained results have shown that the proposed technique is capable of detecting and tracking landmarks with good reliability.

The problem of locating facial landmarks is important in many applications such as security, 3D modeling and expression recognition. In this paper, we present a new facial landmarks detection system. The core of the proposed system is a cascade of a new detector based on correlation filters. This detector inherits from the correlation filters the tolerance to small variations of the desirable pattern. This detector is refereed to as IPD (Inner Product Detector) and, different from the correlation filters, is suitable for features with a small number of dimensions. In our experiments we use cross-validation of 503 images from BioID database. We verify that the proposed method provides competitive performance when compared to Support Vector Machines.

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