## 1 Introduction  
Social robotics is an important area of research and an emerging field in robotics as robots are increasingly landing new roles which are mostly of an assistive nature, namely in education and especially for children with special needs or in elderly care [1, 2, 3, 4]. Improving the human-robot interaction (HRI) in such contexts would require a robot to be more aware of the human that it is interacting with, and the ability to identify people, detect their actions and recognize their faces and emotions in particular would be required at the very least [3]. Hence, there is a need for an integrated approach to robot vision that incorporates action recognition along with face and emotion recognition to make robots more amiable in their social interactions with their human counterparts. Human action recognition using video cameras is a challenging task in computer and robot vision. However, it has wide-ranging applications in health care, sports training,physical rehabilitation of the injured, interactive entertainment and video understanding [5]. Action recognition typically requires the processing of huge amounts of information streaming through the input video that must be analyzed in real time, which is a challenging task. One approach to this problem is to create and use larger architectures [6] which are useful in achieving better precision, whereas a second approach involves getting rid of redundant video data and creating a more concise representation of the raw input video, which would result in reduced computational costs for more accurate action recognition in real time [6]. Action recognition involves at least three steps: the processing of the raw video input, followed by the creation of an appropriate representation for the human actions and finally the classification of those representations into the appropriate action categories. The speed and accuracy of human action recognition can be improved by improving any of these steps in terms of computational speed and accuracy. Multiple studies [7; 8; 9; 10] have indicated that videos contain temporally redundant data, implying that multiple frames of a video can be easily skipped without losing much information on the actions recorded in that video. Moreover, there are some action classes in the standard benchmark datasets that do not require motion or temporal information for their identification. Hence, with only a few key frames as inputs, these actions can be easily identified. Human skeletal features (i.e., points that show the relative placement of different parts or joints of the human skeleton), has been one of the most effective and extensively utilized elements to represent various human actions in recent years. These features can be used for the identification of human action when they are analyzed over multiple frames in the video. Thus, action recognition on the basis of human skeletal features is simply a timed motion analysis of human poses [11]. Different input data can be used to gain information about the skeletal joints, such as a simple RGB video, a spatiotemporal joint trajectory obtained through a sensor system, a 3D skeleton derived from image data or a hybrid system that combines any of the preceding input data sources. Many studies have found it to be very effective in simplifying the network architecture required for action recognition [12; 13; 14]. In this paper, we propose an integrated framework for robot vision that incorporates action recognition with face and emotion recognition. Our methodology for human action recognition involves the extraction of key frames from the input video based upon the gradient of a proposed motion metric that is calculated using dense optical flow. After the key frames are extracted, pose estimation models are then used to extract human skeleton features, also known as human keypoints, from the extracted key frames. These human keypoints are then used to train a deep neural network, which can then be used to identify different human actions.