Editorial: Human-centered robot vision and artificial perception

Abstract

In the daily life of human beings, more than 80% of their awareness of the surrounding environment relies on visual information, and the eyes are the most important sensory system of the human body. Meanwhile, human intentions and states are mostly reflected in non-verbal communication or body language behaviors. Intelligent perception of human modalities can recognize human states and intentions and provide reasonable feedback in virtual reality, driver monitoring systems, patient visual diagnosis, and human-computer interaction applications. As shown in Figure .1, humancentered robot vision and artificial perception mainly includes: gesture visual perception and interaction, body movement visual perception and interaction, gaze direction visual perception and interaction, facial expression visual perception and interaction, and human-object interaction visual perception. Driven by deep learning and big data, artificial perception has made great progress in both theory and application. We publish this research topic to bring together the latest theoretical findings and experimental results in this field. Of all the submissions in this research topic, four manuscripts were accepted after a standard review process. Below we present a brief review of the published articles.Koller et al investigated robotic gaze aversion and its effects on human behavior and attitudes. When humans are asked difficult questions, adults and children tend to avert their eyes at certain points in the interaction. Human-inspired gaze parameters have been used to implement gaze behavior for humanoid robots in conversational settings and to improve user experience (UX). However, it is unclear how deviations from human-inspired gaze parameters affect UX. Based on eye-tracking, interaction duration, and self-reported attitude measures, the authors investigated the impact of nonhuman-inspired gaze duration on participants' UX in a conversational setting. The authors demonstrated that the results of systematically varying the gaze aversion rate (GAR) of a humanoid robot over a wide range of parameters (from 0.1 to 0.9). They found that a low GAR leads to shorter interaction durations and that human participants change their GAR to mimic the robot. Moreover, at the lowest gaze aversion setting, participants did not stare back as expected, suggesting that a high mutual gaze is not always a sign of high comfort. These research findings are useful for designing a robot gaze behavior for human-robot interaction. Wang and Chahl investigated the image-based heart rate estimation method namely imaging-photoplethysmography (iPPG), using three-dimensional (3D) human simulation. Due to the lack of high-quality human datasets, the authors propose an enhanced 3D human model with dynamically similar cardiac signals to those of a real person, which can be used to create a simulated dataset for cardiac signal extraction. The authors integrated all subject variables (e.g., facial expression, blinking, skin type, body activity, etc.) and environmental variables (light changes) into the simulated environment, and body motion caused by involuntary movements and breathing into the 3D human model to make the 3D model closer to the real scene. The authors evaluated the performance of five well-known traditional iPPG methods and four deep learning iPPG methods on a set of 3D human models with different appearances against real database videos. They found that the time and frequency domain signals from the 3D human body were in good agreement with the data from the comparison group (real human video) by various iPPG tests. Zhang et al studied a vision-based trajectory teaching method by recognizing the finger trajectory. It consists of a color camera (RGB), a 3D sensor (D) and a thermal camera (T) for multimodal point cloud (RGB-D-T) perception. Since touching an object with a finger results in a slight temperature change of the object's surface, the authors use the 3D thermal traj