# A Projection-Based Augmented Reality System for Medical Applications
## 1 Introduction
The Pupil Tracking method irradiates the eye with infrared and near-infrared light, and, because the pupil has a low reflectivity for infrared rays, and the iris has a high reflectivity for infrared rays, an image is generated between the pupil and the iris. The brightness increases as the iris becomes larger, while the brightness becomes smaller between the iris and the white of the eye, so it becomes easier to grasp the contour of the pupil. The direction of eyesight can then be determined by detecting the position of the pupil. Similarly, the IROG method also irradiates a row of infrared light on the iris, and, because the sclera almost completely reflects the infrared light relative to the iris and pupil, when the eye rotates, the position of the pupil and the rotation of the eye can be computed using the position and intensity of the light reflected by the sclera. However, infrared light can cause damage to the eye, and methods based on it are susceptible to errors caused by the influence of external light sources. For the position of the observer, face detection is an effective method. There are various features for detection used in different studies, including the Haar cascades [10; 11; 12], which has been demonstrated not to be affected by scales difference or minimal occlusion by hair, but is only suitable for frontal detection, and susceptible to insufficient lighting and partial face occlusion [13]. The Histogram of Oriented Gradients (HOG) was probed [14] for this application, and though it can be used to detect faces at slightly inclined angles, in addition to frontal, it still fails at extreme angles. A Single-Shot-Multibox-detector (SSD) model, combined with deep learning, were examined in [15; 16; 17], and found to successfully detect the faces of different scale changes in the feature maps generated by the deep learning networks, but, due to the limitation of fewer intermittent definite anchor scales, the accuracy in detecting smaller faces may be too low. In determining the location of the observer, a 3D spatial layout detection device would also be very helpful, in addition to facial detection. Though electromagnetic devices have been used for spatial positioning [18], they are not really suitable for medical environments. Recent studies in medical fields have used optical spatial positioning devices, such as NDI's Polaris Vicra optical tracker [19], or Intel RealSenseTM [20] with dual-camera setup, which uses infrared cameras to track position of the patient or the phantom, and uses the CCD cameras to track moving objects in 3D. Image projection-based augmented reality for the application of neurosurgery was proposed fairly early. [PERSON] et al. [21], in 2005, proposed a projection-based augmented reality system, which would project the image of the region of interest in the cranial area onto the surface of the patient's head. One of the problems of such a system is the problem of registration, which is the problem of seeking to align the position of the 3D model in the computer with the real-world coordinate of the patient's head, or a phantom representing a head. In [21], the registration problem was solved by placing five feature markers on the patient's head before surgery, and also marking the positions corresponding to these five markers in the 3D model. In this way, when projecting, the computer only has to achieve perfect alignment with respect to the positions of these five markers to ensure projection accuracy. [PERSON], et.al. [22] proposed another projection-based system consisting of four parts: target tracking, head tracking, target transforming for different viewing angles, and, finally, the projection. The heart of this proposed system is in the construction of the 3D descriptors of the surface contours of the 3D model, and in the transformation of the same model under different viewing angles.