# An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search
## 1 Introduction  
To facilitate proper robot-human interactions, an immediate task is to equip the robot with the capability of recognizing a person in real time [1, 2]. With the development of deep learning technology and the availability of larger image databases, it is possible to achieve a very high facial recognition rate using some benchmark databases; for example, 95.89% for the Labeled Faces in the Wild (LFW) database [3], and a slightly lower recognition rate of 92.35% after projecting the images into 3D models [4]. However, such algorithms are not directly usable for facial recognition in real-world scenarios, because faces should also be recognized from the side, which are heavily affected by affine deformation in 3D alignment, and, from a long distance, the captured face image could be of poor quality and low resolution. Another main challenge for the facial recognition system is \"poor camera focus\" [5, 6]. When the distance from the camera to the subject is increased, the focal length of the camera lens must beincreased proportionally if we want to maintain the same field of view, or the same image sampling resolution. A normal camera system may simply reach its capture distance limit, but one may still want to recognize people at greater distances. No matter how the optical system is designed, there is always some further desired subject distance, and, in these cases, facial image resolution will be reduced. Facial recognition systems that can handle low-resolution facial images are certainly desirable. We believe that humans benefit from the smaller sized facial database maintained in their memory for people recognition and identification [7, 8]. Humans can fully utilize local information such as location and prior knowledge of the known person (e.g., characteristic views), which makes them capable of focusing their attention on more obvious features that are easier to detect [9, 10]. For example, when a person searches for an old man from a group of children, grey hair might be an apparent feature that can be recognized from a far distance and in different directions. Conversely, when a person looks for a young boy from a group of children, hair color might be trivial. Different features should be selected for recognition tasks amongst different groups of people, depending on our prior knowledge of the known person. In this article, we propose an adaptive ensemble approach to improve facial recognition rates, while maintaining low computational costs by combining a set of lightweight local binary classifiers with pre-trained global classifiers. In this approach, we assume that the physical world is covered by a wireless sensor network (WSN) that is comprised of multiple wireless communication nodes. They are distributed in the environment and are aware of local context changes, such as when people come into or depart from its vicinity. Each node maintains a lightweight classifier to best distinguish nearby people. Features of new people are learned by the node as they enter into the communication range of a node, even if only one photo per person is available for training. The robot can collect the lightweight classifiers from the nodes on its way. These lightweight classifiers will then be combined with other pre-trained global classifiers by the robot to find the right features for facial recognition. Our method has been tested in a real-world experiment. Each WSN node is a \"Raspberry Pi3\" credit card-sized computer. The pioneer robot (Pioneer 2-DX8 produced by ActivMedia Robotics, LLC., Peterborough, NH, USA) is used to search people and to recognize them based on snapshots. Figure 1 is an overview of our proposal. The combination of a real-time learnt lightweight local binary classifier with pre-trained global classifiers addresses the extreme unbalance of false-positive results when the classifier is used in local dataset classifications. Furthermore, it reduces the errors that are caused by affine deformation in face frontalization.