A Method of False Alarm Recognition Based on k-Nearest Neighbor

Guan, Fengjiao; Shi, Junyou; Ma, Xiaodong; Cui, Weiwei; Wu, Jie

doi:10.1109/dsa.2017.11

Cited by 6 publications

(1 citation statement)

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“…(a) K-nearest neighbors (k-NN): In this, the underlying assumption is that samples of the same class will be the nearest neighbors of each other, i.e., the distance between them will be small since they are related [ 39 , 40 ]. While using this method, the training samples form vectors in a multidimensional feature space, each with its class label.…”

Section: Identification Of Error Patterns In Eye Gaze Datamentioning

confidence: 99%

MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

Kar

2020

Vision

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Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In previous research on pattern analysis of gaze data, efforts were made to model human visual behaviors and cognitive processes. What remains relatively unexplored are questions related to identifying gaze error sources as well as quantifying and modeling their impacts on the data quality of eye trackers. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.

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Section: Identification Of Error Patterns In Eye Gaze Datamentioning

confidence: 99%