Human-Mobile Robot Interaction in laboratories using Kinect Sensor and ELM based face feature recognition
Hui Liu, Norbert Stoll, Steffen Junginger, Jian Zhang, Mazen Ghandour, Kerstin Thurow · 2016
In this paper, a new human feature based method is proposed for the intelligent HMRI (Human-Mobile Robot Interaction) in the indoor life science laboratories. The proposed method includes the contents as: (a) the Microsoft Kinect Sensors equipped on the mobile robots are adopted to detect the human and measure their face color images; (b) the different face features in the measured face images are defined to recognize the dynamic human face orientations. To find the best one among the available features, their comparison is provided, including the eyebrow zone, the eye zone, the hybrid eyebrow-eye zone and the nose zone; (c) the Extreme Learning Machine (ELM) is built to complete the intelligent computation for the robust recognition of the face orientations; and (d) based on the recognizing results of the face orientations, the human face moving directions are obtained successfully. Since the proposed HMRI strategy is developed for the real-time mobile robot based transportation, the computational accuracy and the real-time performance are both focused in this study. The experimental results indicate that the nose feature based HMRI strategy has the best performance, which can reach the success rate of 99.89% only consuming 0.127s.