Head-mounted devices integrated with eye tracking promise a solution for natural human-computer interaction. However, they typically require per-user calibration for optimal performance due to inter-person variability. A differential personalization approach using Siamese architectures learns relative gaze displacements and reconstructs absolute gaze from a small set of calibration frames. In this paper, we benchmark Siamese personalization on polarization-enabled eye tracking. For benchmarking, we use a 338-subject dataset captured with a polarization-sensitive camera and 850 nm illumination. We achieve performance comparable to linear calibration with 10-fold fewer samples. Using polarization inputs for Siamese personalization reduces gaze error by up to 12% compared to near-infrared (NIR)-based inputs. Combining Siamese personalization with linear calibration yields further improvements of up to 13% over a linearly calibrated baseline. These results establish Siamese personalization as a practical approach enabling accurate eye tracking.
Figures & tables
Figure 1: Intensity
Figure 5: The polarization-based dataset is split into training and testing subjects. Both training and testing employ a Siamese network architecture. During training, the network learns to predict gaze displacement between pairs of images from the same eye of the same subject. During testing, the predicted gaze for an input image is computed by estimating gaze displacements between the input and each image in the subject’s calibration set, then aggregating the calibration gaze labels based on the predicted displacements. While our approach processes binocular images as input to each Siamese branch, we illustrate with single-eye images for clarity.
Linear
Gaze Error ( ∘ ) ↓
Data
Model
Calibration
P50
P75
P95
Polarization
Siamese
✓
0.91
1.51
2.88
×
1.08
1.65
2.98
Baseline
✓
1.05
1.69
3.15
×
2.36
3.18
4.78
Intensity
Siamese
✓
1.03
1.72
3.05
Table 1: Test gaze error percentiles in degrees (P50, P75, P95) comparing Siamese and Baseline models on Polarization (Intensity-DoLP-AoLP) and Intensity data. Siamese personalization outperforms linearly calibrated Baseline, with the combination of Siamese personalization and linear calibration achieving the best overall performance. The proposed Siamese model with Polarization data achieves lower gaze errors than the conventional linearly calibrated Baseline model with Intensity data.
Calibration
Gaze Error ( ∘ ) ↓
Data
Model
Images
P50
P75
P95
Polarization
Siamese
3
1.16
1.76
3.10
5
1.12
1.71
3.04
7
1.11
1.68
3.01
9
1.08
1.65
2.98
Baseline
∼ 100
1.05
1.69
3.15
Table 2: Effect of calibration/anchor image count on P50, P75, and P95 percentile errors in the Siamese pipeline. Performance improves with additional calibration images, with 9 images achieving competitive results compared to linear calibration that utilizes the entire calibration sequence. With Polarization data, both the Siamese personalization and linearly calibrated Baseline models outperform the conventional Intensity-based linearly calibrated Baseline.
Figure 6: P50 (50th percentile/median) of the error distribution
Figure 9: Random Sampling
Gaze Error
Data
Sampling Strategy
P50
P75
P95
Polarization
Random Sampling
1.08
1.65
2.98
Calibration Sampling
1.34
2.05
3.56
Table 3: Performance comparison of random sampling vs. calibration sampling (with 3 Images/Subject) for Siamese model training on Polarization data. Random sampling shows lower gaze errors than pre-selected fixed calibration sets.
Linear
Gaze Error ( ∘ ) ↓
Data
Calibration
P50
P75
P95
Polarization
✓
1.05
1.69
3.15
×
2.36
3.18
4.78
Intensity
✓
1.24
2.02
3.56
×
2.77
3.67
5.32
Single-channel Intensity
✓
1.26
2.04
3.57
Table 4: P50, P75, and P95 percentile errors for different input modalities. Triplicating the Intensity channels reduces error compared to single-channel Intensity input, but Polarization achieves the best performance, demonstrating that the improvement is due to polarization features rather than increased channel count.
Department of Bioengineering, University of California San Diego · Swartz Center for Computational Neuroscience, University of California San Diego · Tsinghua University, University of California San Diego +2