# Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs
## 4 Discussion  
This study proposes a low complexity technique for the detection and tracking of bare and sunglasses-wearing faces. The algorithm results showed high accuracy and fast speed even when eyes were occluded by sunglasses, while the CPU consumption was only 10%. In previous studies, we reported on the performance of a bare-face eye tracker [23]. Although this tracker identified and tracked eyes with high accuracy and speed, its performance deteriorated for users wearing sunglasses. The algorithms proposed herein for eye position estimation using nonoccluded shape points on sunglasses-wearing faces address this issue and showed a marked improvement. Furthermore, the proposed algorithm retains the advantages of low complexity and high speed demonstrated by our algorithms for analyzing bare faces [23]. Moreover, when compared to state-of-the-art deep-learning-based methods such as the practical facial landmark detector (PFLD) [36], which has a precision of 8 mm, our proposed method offers comparable precision (10 mm), reduced CPU consumption (10% down from 25%), and is faster (4 ms down from 12 ms). Figure 7 presents a performance comparison between our proposed algorithm and those reported in other studies. Figure 7: Method comparison. (**a**) Direct eye tracking for bare faces using the SDM+SIFT algorithm [23]. It showed 2 mm precision with 10% CPU consumption at a speed of 2–5 ms per frame. (**b**) Direct eye tracking for sunglasses-wearing faces using the SDM+SIFT algorithm [23]. It showed 25 mm precision with 10% CPU consumption at a speed of 2–5 ms per frame (**c**) Direct eye tracking using a CNN-based PFLD algorithm [36]. It showed 8 mm precision with 25% CPU consumption at a speed of 12 ms per frame. (**d**) Proposed indirect pupil position estimation method, which includes an indirect eye-tracking algorithm that estimates pupil location based on other facial feature points. The proposed method showed 10 mm precision with 10% CPU consumption at a speed of 2–5 ms per frame. One of the main advances of our method compared with previously published works is its low complexity, with only low CPU computation required to provide real-time eye tracking. This is particularly valuable when considering the limited system resources of current vehicles. Additionally, our method offers different mode-switching systems according to user conditions. In that sense, our proposed system structures are highly compatible with other algorithms, including deep neural algorithms. We also conducted experiments involving 20 test subjects to investigate the effects of 3D crosstalk when viewing 3D content generated by the AR 3D HUD prototype while wearing sunglasses (Table 2). None of the users reported experiencing static 3D crosstalk (i.e., user head movements < 250 mm/s) while wearing sunglasses. However, when user head movements exceeded 250 mm/s (i.e., dynamic 3D crosstalk), 11 of the 20 participants reported experiencing 3D crosstalk while wearing sunglasses, while only 2 of the 20 participants reported experiencing dynamic 3D crosstalk while not wearing sunglasses. Considering that the 3D margin of our AR 3D HUD prototype is 12 mm, the 10 mm precision error associated with the proposed method is near the 3D margin boundaries, which could cause delayed left and right 3D view conversion owing to limited system latency. Therefore, further improvements to the eye-tracking precision are required in the future to eliminate the potential for dynamic 3D crosstalk. To validate 3D crosstalk with a large eye-tracking error of 10 mm, we added a random eye position error of <8 mm to the bare-face eye tracker, which had an average mean error of 2 mm (Figure 8). The addition of this random error to our bare-face eye tracker did not lead to users reporting static 3D crosstalk when the eye-tracking precision was <10 mm. This study has a few limitations that we hope to address in subsequent work.