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Abstract A person with impaired emotion recognition is not able to correctly identify facial expressions represented by other individuals. The aim of the present study is to assess eyes gaze and facial emotion recognition in a healthy population using dynamic avatars in immersive virtual reality (IVR). For the first time, the viewing of each area of interest of the face in IVR is studied by gender and age. This work in healthy people is conducted to assess the future usefulness of IVR in patients with deficits in the recognition of facial expressions. Seventy-four healthy volunteers participated in the study. The materials used were a laptop computer, a game controller, and a head-mounted display. Dynamic virtual faces randomly representing the six basic emotions plus neutral expression were used as stimuli. After the virtual human represented an emotion, a response panel was displayed with the seven possible options. Besides storing the hits and misses, the software program internally divided the faces into different areas of interest (AOIs) and recorded how long participants looked at each AOI. As regards the overall accuracy of the participants’ responses, hits decreased from the youngest to the middle-aged and older adults. Also, all three groups spent the highest percentage of time looking at the eyes, but younger adults had the highest percentage. It is also noteworthy that attention to the face compared to the background decreased with age. Moreover, the hits between women and men were remarkably similar and, in fact, there were no statistically significant differences between them. In general, men paid more attention to the eyes than women, but women paid more attention to the forehead and mouth. In contrast to previous work, our study indicates that there are no differences between men and women in facial emotion recognition. Moreover, in line with previous work, the percentage of face viewing time for younger adults is higher than for older adults. However, contrary to earlier studies, older adults look more at the eyes than at the mouth.Consistent with other studies, the eyes are the AOI with the highest percentage of viewing time. For men the most viewed AOI is the eyes for all emotions in both hits and misses. Women look more at the eyes for all emotions, except for joy, fear, and anger on hits. On misses, they look more into the eyes for almost all emotions except surprise and fear.

The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.