# Fast camera focus estimation for gaze-based focus control

arXiv: https://arxiv.org/abs/1711.03306 · HTML: https://mlsanity.com/paper/1711.03306/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2017.

## Built on by (most cited first, 10 of 10)

- [TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types](https://mlsanity.com/paper/2102.02115/) (2021)
- [The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications](https://mlsanity.com/paper/2101.03793/) (2021)
- [A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis](https://mlsanity.com/paper/2101.04318/) (2021)
- [1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning](https://mlsanity.com/paper/2102.01921/) (2021)
- [Maximum and Leaky Maximum Propagation](https://mlsanity.com/paper/2105.10277/) (2021)
- [Tensor Normalization and Full Distribution Training](https://mlsanity.com/paper/2109.02345/) (2021)
- [One step closer to EEG based eye tracking](https://mlsanity.com/paper/2303.06039/) (2023)
- [GroupGazer: A Tool to Compute the Gaze per Participant in Groups with integrated Calibration to Map the Gaze Online to a Screen or Beamer Projection](https://mlsanity.com/paper/2201.07692/) (2022)
- [Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets](https://mlsanity.com/paper/2206.09697/) (2022)
- [A temporally quantized distribution of pupil diameters as a new feature for cognitive load classification](https://mlsanity.com/paper/2303.12757/) (2023)

## Abstract (arXiv)

Many cameras implement auto-focus functionality. However, they typically require the user to manually identify the location to be focused on. While such an approach works for temporally-sparse autofocusing functionality (e.g., photo shooting), it presents extreme usability problems when the focus must be quickly switched between multiple areas (and depths) of interest - e.g., in a gaze-based autofocus approach. This work introduces a novel, real-time auto-focus approach based on eye-tracking, which enables the user to shift the camera focus plane swiftly based solely on the gaze information. Moreover, the proposed approach builds a graph representation of the image to estimate depth plane surfaces and runs in real time (requiring ~20ms on a single i5 core), thus allowing for the depth map estimation to be performed dynamically. We evaluated our algorithm for gaze-based depth estimation against state-of-the-art approaches based on eight new data sets with flat, skewed, and round surfaces, as well as publicly available datasets.