Oleg V. Komogortsev

Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors

In an effort toward standardization, this paper evaluates the performance of five eye-movement classification algorithms in terms of their assessment of oculomotor fixation and saccadic behavior. The results indicate that performance of these five commonly used algorithms vary dramatically, even in the case of a simple stimulus-evoked task using a single, common threshold value. The important contributions of this paper are: evaluation and comparison of performance of five algorithms to classify specific oculomotor behavior; introduction and comparison of new standardized scores to provide more reliable classification performance; logic for a reasonable threshold-value selection for any eye-movement classification algorithm based on the standardized scores; and logic for establishing a criterion-based baseline for performance comparison between any eye-movement classification algorithms. Proposed techniques enable efficient and objective clinical applications providing means to assure meaningful automated eye-movement classification.

Biometric identification via eye movement scanpaths in reading

This paper presents an objective evaluation of various eye movement-based biometric features and their ability to accurately and precisely distinguish unique individuals. Eye movements are uniquely counterfeit resistant due to the complex neurological interactions and the extraocular muscle properties involved in their generation. Considered biometric candidates cover a number of basic eye movements and their aggregated scanpath characteristics, including: fixation count, average fixation duration, average saccade amplitudes, average saccade velocities, average saccade peak velocities, the velocity waveform, scanpath length, scanpath area, regions of interest, scanpath inflections, the amplitude-duration relationship, the main sequence relationship, and the pairwise distance between fixations. As well, an information fusion method for combining these metrics into a single identification algorithm is presented. With limited testing this method was able to identify subjects with an equal error rate of 27%. These results indicate that scanpath-based biometric identification holds promise as a behavioral biometric technique.

Real-time eye gaze tracking with an unmodified commodity webcam employing a neural network

An eye-gaze-guided computer interface could enable computer use by the seriously disabled but existing systems cost tens of thousands of dollars or have cumbersome setups. This paper presents a methodology for real-time eye gaze tracking using a standard webcam without the need for hardware modification or special placement. An artificial neural network was employed to estimate the location of the users gaze based on an image of the users eye, mimicking the way that humans determine where another person is looking. Accuracy measurements and usability experiments were performed using a laptop computer with a webcam built into the screen. The results show this approach to be promising for the development of usable eye tracking systems using standard webcams, particularly those built into many laptop computers.

Body mass index moderates gaze orienting biases and pupil diameter to high and low calorie food images

The primary goal of this study was to examine eye gaze behavior to different kinds of food images in individuals differing in BMI status. Eye-tracking methods were used to examine gaze and pupil responses while normal weight and overweight women freely viewed pairs of different food images: high calorie sweet foods, high calorie savory foods, and low calorie foods. Self-report measures of hunger, state and trait cravings, and restrained eating were also obtained. Results revealed orienting biases to low calorie foods and decreases in pupil diameter to high calorie sweet foods relative to low calorie foods in the overweight group. Groups did not differ in the average amount of time spent gazing at the different image types. Furthermore, increased state cravings were associated with larger pupil diameters to high calorie savory foods, especially in individuals with lower BMIs. In contrast, restrained eating scores were associated with a decreased orienting bias to high calorie sweet foods in the high BMI group. In conclusion, BMI status appears to influence gaze parameters that are less susceptible to cognitive control. Results suggest that overweight individuals, especially those who diet, have negative implicit attitudes toward high calorie foods, especially sweets.

Biometric authentication via oculomotor plant characteristics

A novel biometrics approach that performs authentication via the internal non-visible anatomical structure of an individual human eye is proposed and evaluated. To provide authentication, the proposed method estimates the anatomical characteristics of the oculomotor plant (comprising the eye globe, its muscles and the brains control signals). The estimation of the oculomotor plant characteristics (OPC) is achieved by analyzing the recorded eye movement trajectories via a 2D linear homeomorphic mathematical representation of the oculomotor plant. The derived OPC allow authentication via various statistical methods and information fusion techniques. The proposed authentication method yielded Half Total Error Rate of 19% for a pool of 59 recorded subjects in the best case. The OPC biometric authentication has high counterfeit resistance potential, because it includes both behavioral and physiological human attributes that are hard to reproduce.

Eye movement prediction by Kalman filter with integrated linear horizontal oculomotor plant mechanical model

The goal of this paper is to predict future horizontal eye movement trajectories within a specified time interval. To achieve this goal a linear horizontal oculomotor plant mechanical model is developed. The model consists of the eye globe and two extraocular muscles: lateral and medial recti. The model accounts for such anatomical properties of the eye as muscle location, elasticity, viscosity, eye-globe rotational inertia, muscle active state tension, length tension and force velocity relationships. The mathematical equations describing the oculomotor plant mechanical model are transformed into a Kalman filter form. Such transformation provides continuous eye movement prediction with a high degree of accuracy. The model was tested with 21 subjects and three multimedia files. Practical application of this model lies with direct eye gaze input and interactive displays systems as a method to compensate for detection, transmission and processing delays.

Biometric identification via an oculomotor plant mathematical model

There has been increased interest in reliable, non-intrusive methods of biometric identification due to the growing emphasis on security and increasing prevalence of identity theft. This paper presents a new biometric approach that involves an estimation of the unique oculomotor plant (OP) or eye globe muscle parameters from an eye movement trace. These parameters model individual properties of the human eye, including neuronal control signal, series elasticity, length tension, force velocity, and active tension. These properties can be estimated for each extraocular muscle, and have been shown to differ between individuals. We describe the algorithms used in our approach and the results of an experiment with 41 human subjects tracking a jumping dot on a screen. Our results show improvement over existing eye movement biometric identification methods. The technique of using Oculomotor Plant Mathematical Model (OPMM) parameters to model the individual eye provides a number of advantages for biometric identification: it includes both behavioral and physiological human attributes, is difficult to counterfeit, non-intrusive, and could easily be incorporated into existing biometric systems to provide an extra layer of security.

Complex eye movement pattern biometrics: Analyzing fixations and saccades

This paper presents an objective evaluation of previously unexplored biometric techniques utilizing patterns identifiable in human eye movements to distinguish individuals. The distribution of primitive eye movement features are compared between eye movement recordings using algorithms based on the following statistical tests: the Ansari-Bradley test, the Mann-Whitney U-test, the two-sample Kolmogorov-Smirnov test, the two-sample t-test, and the two-sample Cramer-von Mises test. Score-level information fusion is applied and evaluated by: weighted mean, support vector machine, random forest, and likelihood ratio. The accuracy of each comparison/jusion algorithm is evaluated, with results suggesting that, on high resolution eye tracking equipment, it is possible to obtain equal error rates of 16.5% and rank-1 identification rates of 82.6% using the two-sample Cramér-von Mises test and score-level information fusion by random forest, the highest accuracy results on the considered dataset.

Eye tracking on unmodified common tablets: challenges and solutions

This work describes the design and implementation of an eye tracking system on an unmodified common tablet PC. A neural network eye tracker is employed as a solution to eye tracking in the visible spectrum of light. We discuss the challenges related to image recognition and processing, and provide an objective evaluation of the accuracy and sampling rate of eye-gaze-based interaction with such an eye tracker. The results indicate that it is possible to obtain an average accuracy of 4.42° and a sampling rate of 0.70 Hz with the described system.

Multimodal ocular biometrics approach: A feasibility study

Growing efforts have been concentrated on the development of alternative biometric recognition strategies, the intended goal to increase the accuracy and counterfeit-resistance of existing systems without increased cost. In this paper, we propose and evaluate a novel biometric approach using three fundamentally different traits captured by the same camera sensor. Considered traits include: 1) the internal, non-visible, anatomical properties of the human eye, represented by Oculomotor Plant Characteristics (OPC); 2) the visual attention strategies employed by the brain, represented by Complex Eye Movement patterns (CEM); and, 3) the unique physical structure of the iris. Our experiments, performed using a low-cost web camera, indicate that the combined ocular traits improve the accuracy of the resulting system. As a result, the combined ocular traits have the potential to enhance the accuracy and counterfeit-resistance of existing and future biometric systems.