Felipe M. Lopes Ribeiro

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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(More)
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(More)
In this paper, we propose a fast weak classifier that can detect and track eyes in video sequences. The approach relies on a least-squares detector based on the inner product detector (IPD) that can stimate a probability density distribution for a feature’s location–which fits naturally with a Bayesian estimation cycle, such as a Kalman or(More)