# Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly
## 3 Material and Methods
### Face Recognition Performance  
Face recognition of a visitor should first be performed to deliver visitor information to an elderly person before the door may be remotely opened. Through a precision experiment involving face recognition, a performance test was conducted to determine whether face recognition can be used in a real environment. First, 30 face images of a user were registered in advance (\"label name\"). In addition, 15 face images of a user were registered in advance, but no image information was applied (\"undefined\"). Finally, a user with no face images (\"unknown\") was set. One single user was captured by the camera at a time. The face recognition experiment was conducted by changing the front and side inputs and angle of the face and by applying conditions such as wearing of glasses and occlusions  
Figure 8: “Hide” pose estimation. (**a**) Ground truth joint (red point) and PoseNet predicted joint (green point). (**b**) Head part accuracy. (**c**) Upper body accuracy except left wrist. Figure 9: “Lie” pose estimation. (**a**) Ground truth joint (red point) and PoseNet predicted joint (green point). (**b**) Head part accuracy. (**c**) Upper body accuracy. to the face. The experiment was performed on 300 frames (180 frames for changes in the face angle, 60 frames for face occlusions, and 60 frames for the wearing of glasses) per person. As the experiment was conducted to verify whether the deep-learning model for face recognition can immediately reflect a real environment, processes such as biometric system authentication after face recognition were ignored (i.e., true negatives, such as an incorrect recognition and authentication denial, and false negatives, such as a correct recognition and authentication denial, were excluded). In the experiment, the precision measurement was targeted for a single user. Moreover, when an accurate recognition was successful, it was classified as a successful recognition (true positive, TP); however when an incorrect class was recognized, it was classified as an incorrect recognition (false positive, FP), and when the face of the test subject was not recognized, it was classified as a failure (miss). In this manner, the DR was measured. Furthermore, the results were classified by dividing the angle change, face occlusions, and wearing of glasses in all 300 frames. The DR was calculated using the ratio of true positives to the total number of frames, and the precision rate was calculated using the ratio of true positives to the detected frames (sum of the true and false positives). For a \"label name\" in advance, Refer to Table 2. Despite changing the face angle between \(-90^{\circ}\) and \(90^{\circ}\), the DR was 87.14% and FP not occurred. With eyeglasses, the DR was 95.22%, confirming that the face recognition function was not significantly affected. However, when a major part, or more than one half of the face was occluded (Figure 10b), the DR was approximately 48.64%, demonstrating a considerably large difference. In the case of \"undefined\" with information from only 15 images, the precision decreased noticeably. When the angle was changed, a DR of 74.65% and a precision of 83.95% were observed as shown in Figure 10c,d). For face occlusions, the DR was 36.07% and the precision was a mere 9.09%. Finally, in the case of \"unknown\" users, the average precision was 96.12%, which as high as in the \"label name\" class. This demonstrated high recognition results when there was no matching information in the stored image data.