Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation

Daniele Agostinelli; Thomas Agostinelli; Andrea Generosi; Maura Mengoni

cs.CVMar 25, 2026

Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation

Authors: Daniele Agostinelli, Thomas Agostinelli, Andrea Generosi, Maura Mengoni

Organizations: Department of Industrial Engineering and Mathematical Sciences, Universit`a Politecnica delle Marche, via Brecce Bianche, 12, Ancona, 60131, Italy · Department of Science and Information Technology, Universit`a Pegaso, piazza Trieste e Trento, 48, Napoli, 80132, Italy

Abstract

Appearance-based gaze estimation frequently relies on deep Convolutional Neural Networks (CNNs). These models are accurate, but computationally expensive and act as "black boxes", offering little interpretability. Geometric methods based on facial landmarks are a lightweight alternative, but their performance limits and generalization capabilities remain underexplored in modern benchmarks. In this study, we conduct a comprehensive evaluation of landmark-based gaze estimation. We introduce a standardized pipeline to extract and normalize landmarks from three large-scale datasets (Gaze360, ETH-XGaze, and GazeGene) and train lightweight regression models, specifically Extreme Gradient Boosted trees and two neural architectures: a holistic Multi-Layer Perceptron (MLP) and a siamese MLP designed to capture binocular geometry. We find that landmark-based models exhibit lower performance in within-domain evaluation, likely due to noise introduced into the datasets by the landmark detector. Nevertheless, in cross-domain evaluation, the proposed MLP architectures show generalization capabilities comparable to those of ResNet18 baselines. These findings suggest that sparse geometric features encode sufficient information for robust gaze estimation, paving the way for efficient, interpretable, and privacy-friendly edge applications. The source code and generated landmark-based datasets are available at https://github.com/daniele-agostinelli/LandmarkGaze.git.

Figures & tables

Fig. 1: Pipeline for the extraction of landmark-based dataset. Images are processed to extract landmarks and head pose, which are then used to normalize the data into a virtual camera space.

Panel of Figure 2 — Fig. 2: Example image from the GazeGene dataset [ 38 ] : (a) original and (b) normalized images. In (b), the red arrow indicates the normalized gaze direction, cyan lines are the image principal axis, and green circles denote the $N=20$ landmarks extracted from the MediaPipe face mesh [ 39 ] to represent gaze and head orientation: the right iris (indices 473–477) and eye contour (263, 362, 374, 386), the left iris (468–472) and eye contour (33, 133, 145, 159), and two head anchors given by the nose tip (1) and the glabella (9).

Feature	Holistic MLP	Siamese MLP
Input representation	Global landmarks ( $\mathbf{f}_{G}$ )	Local eyes ( $\mathbf{f}_{L},\mathbf{f}_{R}$ ), geometry ( $\Delta\mathbf{c},\mathbf{f}_{H}$ )
Hidden width (D)	256	64 (per branch)
N. residual blocks (K)	3	3 (per branch)
Dropout (p)	0.1	0.1

TABLE I: Summary of input representation and specific configurations of the MLPs.

Fig. 3: Architectural comparison of gaze estimation models. (a) Holistic Multi-Layer Perceptron (MLP): facial landmarks are processed through an input projection layer, a stack of

K

residual blocks and a final regression head. (b) Siamese MLP: facial landmarks are split into local eye regions, processed by two independent encoders, and fused with geometric context (relative eye positions

\Delta\mathbf{c}

and head anchors

\mathbf{f}_{H}

) via a fusion MLP. (c) XGBoost: a gradient-boosted tree approach using a multi-output regressor for the gaze vector components,

\mathbf{g}^{\prime}=\left({g^{\prime}_{x},g^{\prime}_{y},g^{\prime}_{z}}\right)

, based on global landmark features,

\mathbf{f}_{G}

. (d) Residual block used in (a) and (b), featuring a residual connection around two sets of Linear, BatchNorm, GELU, and Dropout layers.

Dataset	Original samples	Extracted samples	Retention rate
Gaze360 [ 36 ]	$169935$	$132651$	$78.06$ %
ETH-XGaze [ 37 ]	$756540$	$559057$	$73.90$ %
GazeGene [ 38 ]	$1008000$	$832687$	$82.61$ %

TABLE II: Dataset retention statistics. The retention rate indicates the percentage of images where MediaPipe successfully detected the facial landmarks with confidence

\geq 0.8

Panel of Figure 6 — Fig. 4: Distributions of yaw and pitch angles (in degrees) for gaze (first row) and head pose (second row) across the three datasets - Gaze360 (a), GazeGene (b), and ETH-XGaze (c). Columns (left to right) show the distributions of the original, extracted, and excluded samples. Colors represent density values according to the reported colorbars.

	Image-based	Landmark-based
Dataset	ResNet18	XGBoost	Holistic MLP	Siamese MLP
GazeGene	3.76	13.49 (8.71)	9.00 (7.10)	8.91 (7.02)
ETH-XGaze	5.73	11.63 (7.81)	8.54 (6.71)	8.50 (6.59)
Gaze360	12.23	18.81 (14.38)	14.95 (12.66)	14.68 (12.70)

TABLE III: Results of within-domain evaluation. Performance for landmark-based models is presented as Mean Angular Error and Standard Deviation (SD) in degrees ( ∘ ), calculated across all tested samples. Results for the ResNet18 are reported from the literature [ 38 ] . Bold values indicate the best performance.

Fig. 5: Examples of evident errors in face-landmark detection from different datasets (GazeGene [ 38 ] , Gaze360 [ 36 ] , ETH-XGaze [ 37 ] ). Green circles denote the $N=20$ landmarks extracted using MediaPipe [ 39 ] . These errors are likely due to low image resolution, face cropping, or extreme poses.

Panel of Table 9 — TABLE IV: Results of cross-domain evaluation. For each model architecture, rows and columns represent the training and testing datasets, respectively. Performance for landmark-based models are presented as MAE (SD) in degrees ( ∘ ), where SD is calculated across all tested samples. Results for the ResNet18 are reported from the literature [ 38 ] . Bold values indicate the best performance for each test dataset.

Fig. 6: Permutation Feature Importance (PFI) analysis of the siamese MLP trained on ETH-Xgaze, GazeGene, and Gaze360. The importance of each feature group is measured by the increase in mean angular error ( ∘ ) when the corresponding input features are permuted while keeping others constant. Higher values indicate a greater reliance of the model on that specific feature group. Error bars represent the standard deviation over 1000 permutation cycles.

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Investigating Bias and Fairness in Appearance-based Gaze Estimation

Burak Akgül, Erol Şahin, Sinan Kalkan

Dept. of Computer Eng. and ROMER Robotics Center, Middle East Technical University, Ankara, Turkey

Gaze Estimation Algorithmic Fairness

Open paper AskPDF arXiv

While appearance-based gaze estimation has achieved significant improvements in accuracy and domain adaptation, the fairness of these systems across different demographic groups remains largely unexplored. To date, there is no comprehensive benchmark quantifying algorithmic bias in gaze estimation. This paper presents the first extensive evaluation of fairness in appearance-based gaze estimation, focusing on ethnicity and gender attributes. We establish a fairness baseline by analyzing state-of-the-art models using standard fairness metrics, revealing significant performance disparities. Furthermore, we evaluate the effectiveness of existing bias mitigation strategies when applied to the gaze domain and show that their fairness contributions are limited. We summarize key insights and open issues. Overall, our work calls for research into developing robust, equitable gaze estimators. To support future research and reproducibility, we publicly release our annotations, code, and trained models at: github.com/akgulburak/gaze-estimation-fairness

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