Recognizing human facial expression and emotion by computer is an interesting and challenging problem. In this paper, we propose a method for recognizing negative emotions through an appropriate representation of facial features from relevant face regions displayed in video streams and still images. A measure that is sensitive to facial movements is used in predefined regions of interest to detect the negative emotions. The experimentation has been performed on a standard dataset and live video streams and has showed promising results.

In this paper, we present an effective method for human action recognition using statistical models based on optical flow orientations. We compute a distribution mixture over motion orientations at each spatial location of the video sequence. The set of estimated distributions constitutes the direction model, which is used as a mid-level feature for the video sequence. We recognize human actions using a distance metric to compare the direction model of a query sequence with the direction models of training sequences. The experimentations have been performed on standard datasets and have showed promising results.

In this paper, we present an unconstrained visual gaze estimation system. The proposed method extracts the visual field of view of a person looking at a target scene in order to estimate the approximate location of interest (visual gaze). The novelty of the system is the joint use of head pose and eye location information to fine tune the visual gaze estimated by the head pose only, so that the system can be used in multiple scenarios. The improvements obtained by the proposed approach are validated using the Boston University head pose dataset, on which the standard deviation of the joint visual gaze estimation improved by 61:06% horizontally and 52:23% vertically with respect to the gaze estimation obtained by the head pose only. A user study shows the potential of the proposed system.

In this technical demonstration, we present a tool that projects on a target scene the visual gaze of the people passing in front of it. The target scene could be a large plasma screen, an advertising poster, a shelf or a shop window. The projection of the visual gaze corresponds to the coordinates of a region that represents the person's location of interest in the target scene. It is an important problem with many applications that try to understand human behavior in a controlled environment such as in security or customized marketing. The visual gaze projection is influenced by the parameters of the camera, the settings of the target scene, the method used for the estimation of the gaze (e.g. the information about the head pose, the location of eyes centers and corners, or any combination of them), and the visual gaze starting point.

In this paper we present an application of computer vision techniques to obtain specific information about the behaviour of the people passing in front of a target scene. This is done by analyzing videos captured by cameras monitoring an area under surveillance. The target scene can be a large plasma screen, a projected image, an advertising poster or a shop window. An example of the type of information that can be extracted is the number of people passing in the area (possibly even making a stop), those who are interested in the target scene (i.e. looking in its direction), and the specific locations of interest inside the target scene. The person detection counts the number of persons subsequently followed by the person tracking which determines who is stopping and who is moving. The head pose estimation denotes whether they are looking or not at the target scene. Finally the projection of the visual field extracts the location of interest in the target scene. All these tasks are improved by taking in account the environmental information.

This paper presents the use of a template based method in order to make a head pose estimation. As an image classification problem the aim of this kind of techniques is to convert the input head image into a feature vector. The feature vectors of different persons taken at the same pose will serve to learn a head pose classifier. The aim of this work is to estimate the head pose of people looking at a target scene in order to extract the location of their gaze in the scene.

The aim of our work is to extract the location of interest inside a target scene. This is done by analyzing videos captured by cameras monitoring an area under surveillance where people are passing in front of a target scene. We apply a head pose estimation module to the people detected in the scene, which tells us if they are looking or not at the target scene. The projection of the visual field on the target scene which extracts the location of interest, requires a person's head pose and his/her distance from the target scene. This process allows an accurate explanation of the human activity and interest in front of an advertising poster, a shop window or a shelf for example.