This work proposes a novel system for detecting racial landmarks in images using an ensemble of correlation-based filters known as Inner Product Detector (IPD). This work has three main contributions: i) the usage of a bootstrap aggregating algorithm (bagging), to produce a ensemble classifier with higher accuracy when compared with the original IPD detector; ii) a new discriminant function based on the highest IPD mean value calculated from samples positively classified in a voting scheme; iii) and a study to assess the influence of class unbalance over the system performance. The proposed method was evaluated on the BioID and LFPW datasets, achieving an average accuracy of 93.3% in the BioID for both eyes, at 10% of the interocular distance, and accuracies of 85.2% and 81.6% for the left eye and right eyes respectively, on the LFPW database, at 10% of the interocular distance. Since it can detect the eyes at approximately 70 FPS in a Matlab implementation, the proposed method is also fast enough to be used in real time applications. These results were compared to the ones in the state of the art in eye detection - which include methods using deep learning - in terms of accuracy and computational complexity.

In this paper, we investigate how visualization factors, such as disparity, mobility, angular resolution, and viewpoint interpolation, influence the quality of experience (QoE) in a stereoscopic multiview environment. In order to do so, we set up a dedicated testing room and conducted subjective experiments. We also developed a framework that emulates a supermultiview environment. This framework can be used to investigate and assess the effects of angular resolution and viewpoint interpolation on the QoE produced by multiview systems, and provide relevant cues as to how the baselines of cameras and interpolation strategies in such systems affect user experience. Aspects such as visual comfort, model fluidity, sense of immersion, and the three-dimensional (3D) experience as a whole have been assessed for several test cases. Obtained results suggest that user experience in an motion parallax environment is not as critically influenced by configuration parameters such as disparity as initially thought. In addition, extensive subjective tests have indicated that while users are very sensitive to angular resolution in multiview 3D systems, this sensitivity tends not to be as critical when a user is performing a task that involves a great amount of interaction with the multiview content. These tests have also indicated that interpolating intermediate viewpoints can be effective in reducing the required view density without degrading the perceived QoE.

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 particle filter. As a least-squares sliding window detector, it possesses tolerance to small variations in the desired pattern while maintaining good generalization capabilities and computational efficiency. We propose two approaches to integrating the IPD with a particle filter tracker. We use the BioID, FERET, LFPW, and COFW public datasets as well as five manually annotated high-definition video sequences to quantitatively evaluate the algorithms' performance. The video data set contains four subjects, different types of backgrounds, blurring due to fast motion, and occlusions. All code and data are available.

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.

In this paper we investigate how configuration and visualization parameters influence the quality of experience in a 3D interactive environment, more specifically in a motion parallax setup. In order to do so, we designed a dedicated testing room and conducted subjective experiments with a team of evaluators. The tests considered parameters such as different disparities, amount of parallax, monitor sizes and visualization angles. Factors such as visual comfort, sense of immersion and the 3D experience as a whole have been assessed. The users were also asked to execute a task in the 3D motion parallax environment assessing the difficulty to complete the task for different parameter configurations. Obtained results suggest that user experience in an immersive environment is not as critically influenced by configuration parameters such as disparity and amount of parallax as initially thought. They also indicate that a better understanding of how this experience is influenced still requires the design and conduction of more comprehensive testing procedures.

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.