With reference to published research, filed patents, and existing commercial products, this section presents and categorizes personal information that can be inferred from eye tracking data. As a basis for potential inferences, eye tracking devices can record a large variety of gaze parameters.
Some of the most commonly measured eye movements are fixations, saccades, and smooth pursuit eye movements . During a fixation, the eyes are relatively stable and focused on a specific position, allowing for information to be acquired and processed. Saccades are rapid eye movements from one fixation point to another, lasting 30 to 80 ms . Smooth pursuit movements are performed when eyes are closely following a moving visual target. In addition to the spatial dispersion, duration, amplitude, acceleration, velocity, and chronological sequence of such eye movements, many eye trackers capture various other eye activities, including eye opening and closure (e.g., average distance between the eyelids, blink duration, blink frequency), ocular microtremors, pupil size, and pupil reactivity [19, 58]. Furthermore, most eye trackers videotape parts of the user’s face and may thereby capture additional information, such as the number and depth of wrinkles, and a user’s eye shape and iris texture . Therefore, these parameters were also considered in our investigation into the richness and sensitivity of eye tracking data. Fig. 1 provides an introductory overview of common eye tracking measures and the categories of inferences discussed in this paper.
2.1 Biometric Identification
Due to differences in physical oculomotor structure and brain functioning, certain gaze characteristics are unique for every individual, similar to fingerprints, and can thus be exploited for biometric identification [9, 74, 76]. Among other methods, people can be told apart based on distinct patterns of pupil reactivity and gaze velocity , or by comparing their eye movement trajectories when they focus on a moving target – even if the eye activity is only recorded through an ordinary smartphone camera .
Aside from such gaze-based measures, the complex textures and color patterns in a person’s iris are also suitable for biometric identification. This approach, called iris recognition, is being used in a variety of real-world security and surveillance applications and has been recognized as “one of the most powerful techniques for biometric identification ever developed” . Even though their iris scanning capability is usually not advertised, it should be understood that commodity eye trackers often record and process high-resolution images of the user’s iris, which can not only be used to uniquely identify the user but also to deceive iris-based authentication mechanisms and thereby steal the user’s identity .
In cases where a unique identification of an individual is not possible (e.g., because the person is not registered in the recognition system database), other attributes inferred from eye tracking data, such as age and gender (see Sect. 2.6), health condition (see Sect. 2.9), or ethnicity (see Sect. 2.4), can still help to classify the target person into a specific demographic group and thereby approximate the identity .
2.2 Monitoring of Mental Workload and Cognitive Processes
Certain patterns in eye movement, pupil dilation, and eye blinking have been recognized as reliable indicators of mental workload in people of any age [19, 63], sometimes offering higher accuracy than conventional methods like Electroencephalography . Through eye tracking, it is also possible to distinguish a user’s moments of awareness from moments of distraction and mind wandering [31, 84].
Apart from detecting a user’s mental presence and measuring the mere intensity of cognitive processing, eye tracking can also provide insights into specific conscious and unconscious thought processes in a large variety of contexts. Among other mental tasks and activities, ocular measures have been used to study memory retrieval [19, 31], problem solving [31, 75], learning processes [44, 69], the formation of expectations [19, 27], internal reasoning , and mental computations [19, 31].
Eye tracking data can not only – to a certain extent – reveal what we remember, imagine, expect, and think about, but also our specific decision-making strategies [19, 28] and cognitive styles, i.e., individual differences in the way we acquire, process, and interpret information . For example, people can be classified as field-dependent vs. field-independent (people of the latter type pay more attention to detail and exhibit a more analytical approach to processing visual information) , or as verbalizers vs. visualizers (people of the latter type can process visual information, such as images and diagrams, better than textual information) . The gaze-based inference of such cognitive styles is feasible and can achieve high accuracies, as has been confirmed in a recent study by Raptis et al. .
Researchers from the field of cognitive science and experimental psychology have suggested that eye tracking data will not only be used for the real-time analysis but also for the prediction of human decisions and behavior .
2.3 Inference of Personality Traits
Experimental research has shown that it is possible to automatically infer personality traits from eye tracking data [34, 35, 42]. For example, gaze patterns captured during everyday tasks can be used to evaluate users along the so-called Big Five traits, namely openness to experience, conscientiousness, extroversion, agreeableness, and neuroticism [34, 42]. The gaze-based assessment of personality traits is possible not only in binary form (high vs. low) but also in the form of ranges. In , for instance, eye movement analysis was used for the automatic recognition of different levels of curiosity.
Besides the Big Five traits and curiosity, gaze metrics were found to be associated with various other personality traits, including emotional intelligence , indecisiveness , the tendency to ruminate , trait anxiety , sexual compulsivity , boredom susceptibility , and general aggressiveness . Eye tracking has even been used to investigate people’s attachment styles in interpersonal relationships (e.g., secure, withdrawn, fearful, enmeshed) .
Based on data from 428 study participants, Larsson et al.  also suggest that some personality traits, including tendermindedness, warmth, trust, and impulsiveness, are genetically linked to certain iris characteristics, offering – besides gaze behavior – another potential ocular biomarker to analyze people’s personalities.
2.4 Inference of Cultural Affiliation and Ethnicity
It is widely agreed that culture fundamentally shapes human cognitive processing and behavior . Studies have shown that intercultural differences are reflected in certain gaze characteristics [12, 24, 41, 61]. For example, people of different cultural background were found to exhibit discriminative eye-movement patterns when seeking information on search engine results pages , when exploring complex visual scenes [12, 24], and when viewing videos of actors performing cultural activities . Some cultural biases in visual processing are so pronounced that they can still be measured when external stimuli draw attention in an opposite manner to the respective bias .
Additionally, eye movements can reveal a person’s knowledge of certain cultural practices. For instance, in an eye tracking study by Green et al. , Chinese infants exclusively predicted the goal of eating actions performed by an actor with chopsticks, whereas European infants only anticipated that food would be brought to the mouth when eating actions were performed with Western cutlery, as indicated by their predictive gaze shifts towards the actor’s mouth.
Some studies have also investigated how people of different “race”Footnote 2 differ in their viewing behavior [25, 33, 88]. Apart from the fact that video-based eye trackers can directly record the eye color, eye shape, and skin color of a user, it has been observed in eye tracking studies that test subjects view “other-race faces” differently than faces of their “own race” in terms of the facial features scanned (e.g., initial focus and greater proportion of fixation time on the eyes vs. nose and mouth) [25, 88]. Furthermore, researchers have observed characteristic changes in pupil size, which are attributed to elevated cognitive effort during face recognition, when people look at “other-race faces” . Such differences have been reported, for example, between “Black and White observers”  and between “Western Caucasian and East Asian observers”  and could potentially allow inferences about the genetic and ethnic background of eye tracking users.
Eye tracking data may also allow inferences about a user’s native language. For instance, considerable differences in eye movement patterns during reading can be observed between native and non-native speakers of English . Eye tracking can even be used to determine which specific words are difficult to understand for a person . Among other things, such information could help in estimating a subject’s nationality or geographical origin.
2.5 Skill Assessment
Eye tracking has been used extensively in the study of human expertise and to discriminate between performance levels in a variety of areas [30, 31, 69, 75]. For example, gaze behavior can be analyzed to assess reading and listening comprehension skills [10, 92]. During a corresponding task or scenario, eye tracking can also be used to distinguish between experts and novices in chess , several sports , chemistry , mathematics , school teaching , and various medical skills, including surgery, nursing, anesthesia, and radiology .
Among other gaze characteristics, expertise is often associated with systematic eye movement patterns reflecting a specific task strategy , with the targeted inspection of important regions and task-relevant information [30, 75], and with more consistent gaze patterns over consecutive trials of a task .
In some fields, eye tracking has not only been used as a tool to discriminate between people of different skill levels, but also to predict people’s task performance and learning curves [52, 69] and to examine specific learning disabilities, such as mathematical difficulties and dyslexia [31, 85].
2.6 Age and Gender Recognition
Just like physical shape, skin texture, and cognitive abilities, human eyes and visual behavior are fundamentally affected by the aging process [20, 36]. For example, eye tracking studies found age-related differences in people’s visual explorativeness, pupil reactions to certain visual stimuli, and error rates in eye movement tasks [36, 42].
Furthermore, detailed frontal face images, which are typically required for video-based eye tracking, have already been used for automated age estimation, for instance based on wrinkles in the eye area . Dynamic facial expressions, such as smiles, may also be analyzed to infer the age of test subjects . Other parameters utilized for computerized age-group recognition include iris size and iris texture .
As with age, a person’s gender can be reflected in certain eye tracking measures. For instance, studies found systematic gender differences in people’s fixation distribution while viewing natural images (e.g., stills from romance films or wildlife documentaries) , during online shopping , when playing video games , and when viewing sexual stimuli . Researchers have already used such differences in visual behavior to automatically classify the sex of test subjects .
2.7 Inference of Preferences and Aversions
Eye tracking is widely employed to investigate people’s interests, likes, and dislikes. Spontaneous attention to specific objects in a visual scene (e.g., in terms of frequency, duration, and sequence of gaze fixations) is regarded as a natural indicator of interest [19, 74, 87]. For data presentation and analysis, gaze fixations are commonly aggregated into heat maps to quickly identify potential regions and objects of interest .
Besides the focus of visual attention, other eye parameters, such as pupil dilation and blink properties, can also be used to analyze a person’s degree of interest and to distinguish between positive, neutral, and negative responses to visual stimuli . Emotion detection from gaze data, which can assist in analyzing a user’s interests and preferences [55, 83], will be discussed in Sect. 2.8.
Among other things, eye tracking has been used to examine preferences for certain types of gambling , mobile apps , activities of daily living , types of food , colors, geometric shapes, and product designs , pieces of clothing, animals, video game characters, and items of furniture . Beyond mere interest, existing research even suggests that people’s patterns of visual attention reflect their consumption and purchasing behavior .
Eye tracking has also been used extensively in the study of love and sexual desire. For example, researchers have analyzed pupillary responses and the allocation of visual attention to measure levels of sexual arousal and to investigate mating preferences towards specific facial characteristics, age groups, body shapes, body parts, and signs of social dominance [3, 87].
Apart from positive interests, visual attentional biases captured by eye trackers can also reflect a person’s phobias and aversions (e.g., fear of spiders) [3, 37]. Some interests and preferences can already be inferred from eye tracking data with high accuracy [56, 73, 87] and several patents have been filed in this field [3, 83].
2.8 Detection of Short- and Medium-Term User States
Moods and Emotions.
Eye tracking is increasingly used in the interdisciplinary field of affective computing, where systems are developed to automatically recognize human emotions based on physiological signals and behavioral cues [73, 83]. It has been shown that various ocular measures, including pupil size, blink properties, saccadic eye movements, and specific biases of visual attention, can contain information about a person’s emotional state [4, 23, 55, 59].
Gaze data can reflect emotional arousal and the valence of emotions (positive, negative, neutral) [19, 55] as well as more specific affective states, such as happiness and enthusiasm , acute stress and worry , humorous moods and disgust , curiosity , distress, nervousness, and hostility , fear, anger, sadness, and surprise .
Eye tracking can not only be used to detect emotions with high accuracy  but also to estimate the intensity of emotions [55, 83]. Based on gaze parameters, existing methods can even distinguish whether a user’s emotional response to a given stimulus is rational or purely instinctive .
Fatigue and Sleepiness.
For over two decades, there have been approaches to automatically derive a person’s level of sleepiness from certain ocular measures, such as blink rate, blink duration, average distance between the eyelids, fixation durations, and velocity of eye movements . Recent studies have confirmed the suitability of eye tracking measures as indicators for sleepiness and fatigue [63, 89]. Corresponding methods have already been patented and achieve high accuracies – not only while the user is working on specific cognitive tasks, but also during everyday natural-viewing situations [57, 89].
The consumption of alcohol and other recreational drugs can have measurable effects on various eye and gaze properties, such as decreased accuracy and speed of saccades, changes in pupil size and reactivity, and an impaired ability to fixate on moving objects [29, 67, 85].
Apart from alcohol, significant abnormalities in oculomotor functioning were found in people under the influence of nicotine, 3,4-methylenedioxymethamphetamine (“MDMA”), and tetrahydrocannabinol (“THC”) [29, 70].
Researchers have demonstrated the ability to differentiate between drug-impaired and sober subjects with high accuracy based on eye tracking data . The magnitude of some ocular effects is closely associated with the amount of drugs consumed  and certain effects can even be detected at non-intoxicating doses . In addition to pupillary changes and eye movement impairments, an attentional bias towards drug-related visual stimuli has been observed among intoxicated test subjects .
Not only a state of intoxication, but also an acute state of drug deprivation and craving can have a distinct effect on certain eye tracking parameters [29, 70].
2.9 Health Assessment
Many diseases and medical conditions directly affect the eyes, or parts of the brain that are responsible for oculomotor function, and thereby cause gaze impairments [3, 19, 30]. Characteristic eye movement patterns were found, for example, in people suffering from concussion , fetal alcohol syndrome , irregular growth , chronic pain , neurocognitive impairment due to preterm birth , multiple sclerosis , Alzheimer’s disease [30, 43], Tourette syndrome , Parkinson’s disease , and various vision disorders (e.g., myopia, farsightedness, and blind spots) [3, 43].
As filed patents and published experimental studies show, eye movement analysis can be used to diagnose, monitor, prognose, and sometimes even predict various health disorders [30, 43] which can be subsumed under the umbrella term ETDCC (“Eye Tracking-Relevant Diseases, Conditions, and Characteristics”) .
Research has further demonstrated that certain patterns in gaze orientation and pupil reactivity to food-related stimuli (e.g., high vs. low calorie food images) can be indicative of overweight and obesity .
Abnormal eye movements can be used as behavioral biomarkers for the diagnosis of various mental health problems [1, 5, 29]. Oculomotor dysfunctions and gaze peculiarities are found, for example, in sufferers of anxiety disorder , depression , bipolar disorder , borderline personality disorder , schizophrenia , obsessive–compulsive disorder , binge-eating disorder , ADHD , mild cognitive impairment , autism , and posttraumatic stress disorder .
Some common symptoms of mental disorders are irregularities in blink rate and blink duration , abnormal stability and dispersal of gaze fixations during free viewing , unusual biases of visual attention , impaired smooth pursuit eye-movement performance , eye contact avoidance, and abnormal distance between the eyelids .
Certain mental illnesses, including depression and schizophrenia, can already be detected automatically via eye tracking [1, 5, 30] and corresponding methods have been filed as patents . Besides the possibility of binary classification (suffering vs. not suffering), some ocular measures are associated with the severity of mental disorders . Not only acute disorders can be reflected in gaze data, but also past mental health issues and even the personal risk of future outbreaks [71, 78]. For example, researchers have observed characteristic gaze patterns in previously depressed individuals  and found biases in visual attention that were predictive of future depression scores at a delay of more than two years .
Substance Use Disorders.
Apart from acute states of intoxication (which we have discussed in Sect. 2.8), eye tracking data may contain information about a user’s longer-term drug consumption habits and addictions. Numerous eye tracking studies have reported a strong attentional bias towards drug-related visual cues in addicts of cocaine , alcohol , cannabis , and tobacco [18, 70].
Among other possible methods, such attentional biases can be detected by measuring how quickly, how often, and for how long a person’s eyes fixate on corresponding stimuli in comparison to neutral stimuli, or by testing the person’s ability to look away from drug-related stimuli on command [16, 18]. Significant biases have not only been observed in long-term addicts but also in habitual drug users without clinical symptoms of dependency [18, 67]. The strength of attentional biases towards drug-related visual cues was found to be correlated with scores on drug use scales, such as the Obsessive Compulsive Cocaine Scale  and with self-reported lifetime drug consumption . Research has also shown that certain biases in visual attention can be predictive of craving and even relapse in drug addiction .