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

• URL: http://arxiv.org/abs/2603.24724v1
• Date: Wed, 25 Mar 2026 18:51:09 GMT
• Title: Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation
• Abstract summary: Appearance-based gaze estimation frequently relies on deep Convolutional Neural Networks (CNNs)<n> Geometric methods based on facial landmarks are a lightweight alternative, but their performance limits and generalization capabilities remain underexplored in modern benchmarks.<n>We introduce a standardized pipeline to extract and normalize landmarks from three large-scale datasets.
• Score: 1.1666234644810893
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• SAND: Spatially Adaptive Network Depth for Fast Sampling of Neural Implicit Surfaces [91.71563221166417]
  Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost.<n>We propose an efficient neural implicit geometry representation framework with spatially adaptive network depth.
  arXiv  Detail & Related papers  (2026-04-15T14:14:17Z)
• GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks [84.86699025256705]
  We present GEOBench-VLM, a benchmark specifically designed to evaluate Vision-Language Models (VLMs) on geospatial tasks.<n>Our benchmark features over 10,000 manually verified instructions and spanning diverse visual conditions, object types, and scales.<n>We evaluate several state-of-the-art VLMs to assess performance on geospatial-specific challenges.
  arXiv  Detail & Related papers  (2024-11-28T18:59:56Z)
• TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning [36.725822223732635]
  We propose TorchSpatial, a learning framework and benchmark for location (point) encoding.<n>TorchSpatial contains three key components: 1) a unified location encoding framework that consolidates 15 commonly recognized location encoders; 2) the LocBench benchmark tasks encompassing 7 geo-aware image classification and 10 geo-aware image regression datasets; and 3) a comprehensive suite of evaluation metrics to quantify geo-aware model's overall performance as well as their geographic bias, with a novel Geo-Bias Score metric.
  arXiv  Detail & Related papers  (2024-06-21T21:33:16Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• Strengthening structural baselines for graph classification using Local Topological Profile [0.0]
  We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. We propose a new baseline algorithm called Local Topological Profile (adam), which extends LDP by using additional centrality measures and local descriptors.
  arXiv  Detail & Related papers  (2023-05-01T08:59:58Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework [55.40001810884942]
  We introduce a pure residual network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively. On the real-world ScanObjectNN dataset, our method even surpasses the prior best method by 3.3% accuracy. Compared to most recent CurveNet, PointMLP trains 2x faster, tests 7x faster, and is more accurate on ModelNet40 benchmark.
  arXiv  Detail & Related papers  (2022-02-15T01:39:07Z)
• Spelunking the Deep: Guaranteed Queries for General Neural Implicit Surfaces [35.438964954948574]
  This work presents a new approach to perform queries directly on general neural implicit functions for a wide range of existing architectures. Our key tool is the application of range analysis to neural networks, using automatic arithmetic rules to bound the output of a network over a region. We use the resulting bounds to develop queries including ray casting, intersection testing, constructing spatial hierarchies, fast mesh extraction, closest-point evaluation.
  arXiv  Detail & Related papers  (2022-02-05T00:37:08Z)
• Deep Implicit Surface Point Prediction Networks [49.286550880464866]
  Deep neural representations of 3D shapes as implicit functions have been shown to produce high fidelity models. This paper presents a novel approach that models such surfaces using a new class of implicit representations called the closest surface-point (CSP) representation.
  arXiv  Detail & Related papers  (2021-06-10T14:31:54Z)
• Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks [22.936590869919865]
  We propose improvements in point representations and local neighborhood graph construction within the general framework of graph neural networks. We show that the proposed network achieves faster training convergence, i.e. 40% less epochs for classification.
  arXiv  Detail & Related papers  (2021-03-28T21:34:59Z)
• Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild [104.61677518999976]
  We propose Pixel-in-Pixel Net (PIPNet) for facial landmark detection. The proposed model is equipped with a novel detection head based on heatmap regression. To further improve the cross-domain generalization capability of PIPNet, we propose self-training with curriculum.
  arXiv  Detail & Related papers  (2020-03-08T12:23:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.