# An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search
## 5 Real-World Experiments
In our real-time application, the wireless sensor environment helped to simplify the tasks for the robot, and made it focus more specifically on a small group of people in the vicinity. The location of the iTags could be estimated, and their identities were broadcast across the environment. When the WSN node detected a new face, it took around 11 s to expand a single face photo to nine different poses using the 3 DMM method, and around 6 s to train the local SVM classifier. No apparent time delays occurred during facial recognition. Given the feature vector of a face photo, the robot could combine classifiers and output the results within 5 ms. The robot was fully prepared to recognize all of the people in the environment; meanwhile, it monitored a specific area rather than looking around randomly. Figure 7 shows the details of our experiments:  
1. Office layout and the wireless sensor environment. Guided by the WSN node information, the robot searched the bottom right area of the displayed map. 2. Partially enlarged map and the robot trajectory. 3. The robot with the pan/tilt camera on its top. 4. A snapshot at position D. Blocked by the computer screen, robot did not find anyone. Photo 1 is the target person and his frontalized photos by 3 DMM. Photo 2 is one of our colleagues. Another 28 face photos for training and recognition came from the YaleB database. 5. A snapshot at position E. One face was detected at a distance of three meters (photo 3). Though it was very low in resolution, our method gave negative votes, because it was not the target person. 6. A snapshot at position F. For a moving robot, faces may occur at the corner of the snapshot photo, and they may be easily missed (Photo 4). 7. A snapshot at position G. The target was detected and classified as a true positive by our method with the combined SVM; this would otherwise have been classified as a negative result by the single global SVM approach. 8. Moving forward to position H, and a better quality face photo was obtained. The robot finally found the target people. We compared our method with a single pre-trained SVM in real-world experiments. The robot was asked to search for people in an office. It moved at 0.2 m/s indoors, and spent around 150 s to create a full view (13 snapshots at 340\({}^{\circ}\) rotation) of the surrounding environment. The results were based on 24 experiments, and can be found in Table 5.  
Figure 7: Real-world experiments. The photos were the actual snapshots taken by the camera mounted on the robot. The low resolution blurred face photos are the targets our method is tackling. In real-world implementations, our locally assembled face recognizer obtained a stable and good result for facial recognition. It could find targets in low quality photos caught from a far distance, and the recognition rate was 80% at around 9 m. Meanwhile, it took fewer snapshots and facial recognition processes, because the false positive recognition rate was kept at a relatively low level. Equipped with our face recognizer, the robot could detect people from a far distance, so that it did not have to move near people to obtain a better line of sight every time. Moreover, it also saved much time by avoiding mistaken identities. In contrast, when we used a single pre-trained SVM as the face recognizer, the robot recognized the wrong people, and it spent more time approaching the wrong target. The side face photo may have contained affine deformation, especially when it appeared near the photo frame. The camera needed to rotate slowly to obtain a target-centered photo. Once it missed the target, the robot needed to carry out a complete search again, that was why a single pre-trained SVM took more snapshots and spent more time on facial recognition than our method in the experiment. Although our method also met the problem of distorted face photos, our locally combined binary classifier correctly selected the face features, and it showed better recognition rate for familiar people in its vicinity.