Eye Placement Bias Is Remarkably Robust

Bias Robust Estimation of Scale Where Location Is Unknown

In this paper we consider the problem of robust estimation of the scale of the location residuals when the "true" underlying distribution of the data belongs to a contamination neighborhood of a parametric location-scale family. First we show that a scaled version of the l\tlADAM (median of absolute residuals about the median) is approximately most bias-robust within the class 0 Huber's proposa...

The human eye is an example of robust optical design.

In most eyes, in the fovea and at best focus, the resolution capabilities of the eye's optics and the retinal mosaic are remarkably well adapted. Although there is a large individual variability, the average magnitude of the high order aberrations is similar in groups of eyes with different refractive errors. This is surprising because these eyes are comparatively different in shape: Myopic eye...

Robust Eye Gaze Estimation

Eye gaze detection under challenging lighting conditions is a non-trivial task. Pixel intensity and the shades around the eye region may change depending on the time of day, location, or due to artificial lighting. This paper introduces a lighting-adaptive solution for robust eye gaze detection. First, we propose a binarization and cropping technique to limit our region of interest. Then we dev...

Min-max Bias Robust Regression

This the problem maximum asymptotic bias of regression estimates over a-contamination for the joint of the response carriers. Two classes of estimates are treated: (1) Msestimates with bounded function p applied to the scaled residuals, using a very general class of scale estimates, and (2) Bounded influence function type generalized M-estimates. Estimates in the first class are oblta1rled as p...

Bias Robust Estimation of Scale