367 research outputs found

    Representation of the Consumer Interest in the Federal Government

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    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

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    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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    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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