1. Enhancing User identification during Reading by Applying Content-Based Text Analysis to Eye- Movement Patterns
Akram Bayat Amir Hossein Bayat Marc Pomplun
  University Of Massachusset Boston
Iran University of Science and Technology, Iran

2. Motivation 1. What is our research question?
This work presents a novel technique to identify unique individual readers based on an effective fusion scheme that combines fixation patterns with syntactic and semantic word relationships in a text.
Information Fusion for user identification
Semantic text content
Eye-movement data

3. Eye-Movement Based Identification
Eye- Movement is produced mostly by an individual’s brain and is virtually impossible to imitate.
Applications such as personal security, access restriction, and personalized interfaces.
Eye-Movement during Reading activity :
Advantage : common activity –possibility of performing covert identification
Disadvantage : text content influences reading process that makes it very challenging to obtain invariant features from eye-movement data.

4. Text-content influences classification accuracies
Likely Captures differences between passages instead of differences between individuals
Likely to be over fitted to text content
1- Results based on “Biometric identification through eye-movement”, (AHFE 2017) Akram Bayat and Marc Pomplun

5. What is our solution?
We address this issue by fusing text information into eye movement data. The reason is that we need to account for the different text content and characteristics. Therefore, we not only perform our identification algorithm in a variety of text contents and characteristics, but also fuse semantic content of text with eye movement data.

6. Introduction: What is Eye Tracking?
Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head.
Images from : ,

7. Introduction: Eye Movement in Reading
Two common activities in reading :
A fixation : is the maintaining of the visual gaze on a single point on the screen
A saccade : is a quick movement of gaze between two fixations.
This research focuses on eye-movement behavior
during reading from different types of texts.

8. Experimental Design 40 Subjects
6 Passages, each passage is displayed in 3 screens
(25 female) with an average age of 20.4
All screens were presented on a 22-inch View-Sonic LCD monitor with a refresh rate of 75 Hz and a resolution of 1024 x 768 pixels. Eye movements were monitored by an SR Research EyeLink-2k system with a sampling frequency of 1000 Hz.

9. Eye-Movement in reading
During reading, the eye-movement guidance system directs the gaze to a location near a word in order to identify that word.
Fixation point depends to :
Individuals‘ visual system
Text characteristics : length and frequency of a word
Fixation in different distances to the word: landing site

10. Eye-Movement Data
The mean number of fixations and the mean duration of fixations in each 20 bounding box ( a bag of 20 words) account for eye movement data :
leave out noise and discover the important differences
Fixation positions for a sample subject during reading a sample text. Circles correspond to the fixations. A bounding box around each word is considered as a proper landing site of fixations for that word

11. Which eye-movement feature
Which eye-movement feature ? A significant difference in the mean fixation durations in each bounding box between two different subjects confirms the importance of using the mean fixation duration to distinguish two different subjects.
The mean fixation Durations in each bounding box around a word in a sample screen for two different subjects. In order to smooth the data, we take an average of the mean fixation duration over a bag of 20 words on the screen.

12. Word Vector space model
Vector space models represent words in continuous vector space where semantically similar words assigned to nearby words.
Word embedding models : neural-network based Word2vec models : use skip-gram model
pre-trained Google News corpus word vector model consists of 3 million 300-dimensional English word vectors
vector (”King”) - vector(”Man”) + vector(”Woman”)
Vector(“Queen”)

13. Semantic word representation
The passages exhibit a various range of difficulty levels
(plain English, Fairly difficult, difficult) that were previously
computed by the Flesch readability ease score algorithm
We visualize all 300-dimensional vectors corresponding to all words in all six passages using t-Distributed Stochastic Neighbor Embedding (t-SNE)
Word2Vec
The embedding of all words in six passages (Passage 1 through 6) using skip- gram model where there is a word associated with a data point

14. Where N is the number of words in each screen
Information Fusion
n: number of bags of words in each screen
Where N is the number of words in each screen
We introduce our novel and effective fusion scheme that combines fixation patterns with syntactic and semantic word relationships in a text

15. User Classification
This feature vector similarly is measured in all six passages and for each subject. In this way, For each subject and for a set of six passages we have a set of eighteen, 300-dimentional feature vector.
PCA : first 30 principle components
Each subject’s data is separated to train and test data (3–1 train/test splits) in three different ways. By combining Logistic and Multilayer perceptron as our classification algorithms, we reached an overall accuracy of 96.84% .

16. Conclusion
This work presents a novel technique to identify unique individual readers based on an effective fusion scheme that combines eye movement patterns with semantic contents in a text.
Previous eye-movement identification methods for reading used intricate eye-movement variables that are sensitive to various factors unrelated to reader identification.
The identification result suggests that the extracted features using this technique differ systematically across individuals, which leads to high and consistent identification accuracy. decrease the effect of the text content on the identification procedure.