Qualitative and Quantitative Scoring and Evaluation of the Eye Movement Classification Algorithms
Date
2009-09-16
Authors
Komogortsev, Oleg
Jayarathna, Sampath
Koh, Do Hyong
Gowda, Sandeep Munikrishne
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Abstract
This paper presents a set of qualitative and quantitative scores designed to assess performance of the various eye movement classification algorithms. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (I-HMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the \"best\".
Description
Keywords
eye movements, classification, algorithm, analysis, scoring, metrics, Computer Science
Citation
Komogortsev, O. V., Jayarathna, S., Koh, D. H., & Gowda, S. M. (2009). Qualitative and quantitative scoring and evaluation of the eye movement classification algorithm (Report No. TXSTATE-CS-TR-2009-16). Texas State University-San Marcos, Department of Computer Science.