367 research outputs found
Representation of the Consumer Interest in the Federal Government
We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201
Visual focus of attention estimation using eye center localization
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Visual focus of attention estimation using eye center localization
Estimating people visual focus of attention (VFOA) plays a crucial role in various practical systems such as human-robot interaction. It is challenging to extract the cue of the VFOA of a person due to the difficulty of recognizing gaze directionality. In this paper, we propose an improved integrodifferential approach to represent gaze via efficiently and accurately localizing the eye center in lower resolution image. The proposed method takes advantage of the drastic intensity changes between the iris and the sclera and the grayscale of the eye center as well. The number of kernels is optimized to convolute the original eye region image, and the eye center is located via searching the maximum ratio derivative of the neighbor curve magnitudes in the convolution image. Experimental results confirm that the algorithm outperforms the state-of-the-art methods in terms of computational cost, accuracy, and robustness to illumination changes
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