This paper describes a face detection system which goes beyond traditional face detection approaches normally designed for still images. The system described in this paper has been designed taking into account the temporal coherence contained in a video stream in order to build a robust detector. Multiple and real-time detection is achieved by means of cue combination. The resulting system builds a feature based model for each detected face, and searches them using the various model information in the next frame. The experiments have been focused on video streams, where our system can actually exploit the benefits of the temporal coherence integration. The results achieved for video stream processing outperform Rowley-Kanade's and Viola-Jones' solutions providing eye and face data in real-time with a notable correct detection rate, approx. 99.9% faces and 87.5% eye pairs on 26338 images.

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The human face provides useful information during interaction, therefore any system integrating Vision Based Human Computer Interaction requires fast and reliable face and facial feature detection. Dierent approaches have focused on this ability but only open source implementations have been extensively used by researchers. A good example is the Viola-Jones object detection framework that particularly in the context of facial processing has been frequently used. The OpenCV community shares a collection of public domain classiers for the face detection scenario. However, these classiers have been trained in dierent conditions and with dierent data but rarely tested on the same datasets. In this paper we try to ll that gap by analyzing the individual performance of all those public classiers presenting their pros and cons with the aim of dening a baseline for other approaches. Solid comparisons will also help researchers to choose a specic classier for their particular scenario. The experimental setup also describes some heuristics to increase the facial feature detection rate while reducing the face false detection rate.Keywords face and facial feature detection · haar wavelets · human computer interaction · face datasets · OpenCV

Abstract. Perceptual User Interfaces (PUIs) aim at facilitating humancomputer interaction with the aid of human-like capacities (computer vision, speech recognition, etc.). In PUIs, the human face is a central element, since it conveys not only identity but also other important information, particularly with respect to the user's mood or emotional state. This paper describes both a face detector and a smile detector for PUIs. Both are suitable for real-time interaction. The face detector provides eye, mouth and nose locations in frontal or nearly-frontal poses, whereas the smile detector is able to give a smile intensity measure. Experiments confirm that they are competitive with respect to extant detectors. These two detectors are used in an unobtrusive application that allows to interact with an Instant Messaging (IM) client.

This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A novel open source framework is presented for the development of 3D graphics applications over large geographic areas, offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. formation. The user is enabled to simulate and visualize a wildfire spreading on the terrain integrating spatial information on topography and vegetation types with weather and wind data. The application communicates with a remote web service that is in charge of the simulation task. The user may specify several parameters through a friendly interface before the application sends the information to the remote server responsible of carrying out the wildfire forecasting using the FARSITE simulation model. During the process, the server connects to different external resources to obtain up-todate meteorological data. The client application implements a realistic 3Dvisualization of the fire evolution on the landscape. A Level Of Detail (LOD) strategy contributes to improve the performance of the visualization system.

Abstract. In this paper an architecture for an estimator of short-term wind farm power is proposed. The estimator is made up of a Linear Machine classifier and a set of k Multilayer Perceptrons, training each one for a specific subspace of the input space. The splitting of the input dataset into the k clusters is done using a k-means technique, obtaining the equivalent Linear Machine classifier from the cluster centroids. In order to assess the accuracy of the proposed estimator, some experiments will be carried out with actual data of wind speed and power of an experimental wind farm. We also compute the output of an ideal wind turbine to enrich the dataset and estimate the performance of the estimator on one isolated turbine.

Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform. The set of sensors is set up combining the rich data source offered by a Kinect sensor, which provides vision and depth at low cost, and a thermopile array sensor. Experimental results carried out with a mobile platform in a manufacturing shop floor and in a science museum have shown that the false positive rate achieved using any single cue is drastically reduced. The performance of our algorithm improves other well-known approaches, such as C4 and histogram of oriented gradients (HOG).

Low cost real-time depth cameras offer new sensors for a wide field of applications apart from the gaming world. Other active research scenarios as for example surveillance, can take advantage of the capabilities offered by this kind of sensors that integrate depth and visual information.In this paper, we present a system that operates in a novel application context for these devices, in troublesome scenarios where illumination conditions can suffer sudden changes. We focus on the people counting problem with re-identification and trajectory analysis.Automatic people counting offers different application contexts related to security, safety, energy saving or fraud control. Here we go one step further and give hints to extract useful information using depth cameras. The processing of that information allows us to analyze the individuals behavior, particularly if they go away from the typical trajectory, and the problem of re-identifying people.