The eye gaze analysis represents a challenging field of research, since it offers a reproducible method to study the mechanisms of the brain. Eye movements are arguably the most frequent of all human movements and an essential part of human vision: they drive the fovea and consequently, the attention towards regions of interest in space. This enables the visual system to fixate and to process an image or its details with high resolution: act of fixation. This chapter investigates some common techniques and algorithms to study human vision.

1. PATTERN RECOGNITION
ON HUMAN VISION
GiacomoVeneri
Dept. of Applied Neurological Science, University
of Siena
viale Banchi di Sotto 55, 53100, Siena, Italy
Recent Res. Devel. Pattern Rec., 5(2013): 19-47
ISBN: 978-81-7895-584-1

2. Abstract
• This chapter’s book investigates some common techniques
and algorithms to study human vision.
http://www.ressign.com/UserArticleDetails.aspx?arid=1
2023

3. Summary
• Eye movements features
extraction
• Saccades identification
• Optimisations
• Saccades applications:
Trajectories
• Fixations identification
• Velocity threshold algorithm,
Distance Dispersion Algorithm,
Covariance dispersion algorithm,
Minimum spanning trees,
Fixations clustering
• Microsaccades identification
• Eye movements filtering
• Noise filtering, Spike removal filter
• Nystagmus identification
• Eye-movements pattern
identification
• Scan path
• Fixations distribution on predefined
regions of interest
• Regions of interest extraction
• Other methods:Attention models

4. Introduction
Eye-tracking technologies
• There are several categories
of eye movement
measurement methodologies
involving the use or
measurement of: Electro
Oculo Graphy (EOG), Photo
Oculo Graphy (POG), Video
Oculo Graphy (VOG), Scleral
Contact Lens (SCL), Search
Coil (SCG), and the most
common video-based
combined pupil/corneal
reflection (VCR)
Interactive systems
• Gaze-contingent displays GC
and applications, have been
described by several articles
and have been used in
various applications, such as
reading, virtual reality,
images and scenes
perception, computer
graphics, rehabilitation and
visual search studies.
• These applications change the
display according to the line os
sight.

5. Saccades identification
Fisher and Biscardi Methods
• The method (algorithm 1)
is a two stage procedure;
the process begins by
calculating point -to-point
velocities for each point of
the data set.
Optimisation
• Behrens and later Behrens
and MacKeben proposed an
algorithm based on
acceleration.
Fixation
Saccade

6. Saccades applications:
Trajectories
• Saccade identification is the requisite to evaluate the saccade
trajectory. Different methods have been used throughout
the literature to quantify saccade trajectories. A recent paper
[83] has compared many of these methods: some measures
include all sample points on the trajectory of the saccade (area
curvature and quadratic curvature), while others focus on one
specific sample (saccade deviation, initial direction or saccade
endpoint).
Trajectory

7. Fixations identification
Common Methods
• The most common
algorithms are based on
cluster analysis, velocity
based or dispersion
thresholding: Distance
Dispersion Algorithm,
Centroid-Distance Method,
Position-Variance Method
and Salvucci I-DT Algorithm
Covariance dispersion algorithm
• Veneri, Piu et al. used the
mutual information between
the axis X andY of the data
set: in human visual search the
source of variability should be
due to the same system; the
key principle of the proposed
technique is based on
supposing x and y
independent with the same
variance during a fixation
(Algorithm 3).

8. Fixations identification

9. Microsaccades identification
• Microsaccades can be detected in eye movement
recordings when a participant is fixating a stationary object.
While small drifts induce a rather erratic trajectory,
microsaccades are ballistic movements and create small
linear sequences embedded in the trajectory. Microsaccades
occur at a rate of 12 per second and have a typical
amplitude between 1deg and 2.5deg.
• Engbert and Kliegl developed a new algorithm for the
detection of microsaccades in two-dimensional (2D) velocity
space.

10. Eye movements filtering
Noise filtering
• Kumar et al. applied a simple FIR
filter and an outlier and a saccade
detectors algorithms to manage
the number of the gaze points in
the averaging buffer.When the
threshold is exceeded, the filter
buffer is cleared.
• Veneri et al. and Jimenez, et al.
used a weighted averaging filter
switchin on/off according to the
gaze features. Savitzky-Golay
filter was used by Nystrom and
Holmqvist.
• Komogortsev and Khan reported
about successfully applied a Kalman
filter for smoothing gaze path.
Spike removal filter
• Spike removal: the algorithm
removes unwanted artifact
due to disease or eye tracking
procedure

11. Nystagmus
identificati
on
Nystagmus is a type of
eye movement that may
be induced through
stimulation of the
vestibular system. It is
characterized by to
horizontal
and/or vertical motion of
the eyes.
Most of the developed
nystagmus techniques are
based on the evaluation
of the direction or the
velocity of fast phase
components

12. Eye-movements pattern
identification
Scan Path
• The Scan-path was one of the first
methods to identify patterns of
eye movement: Noton and Stark
defined a number of spatial
Regions of Interest (ROIs) in the
scene being scanned and
recoding the fixation sequence
as a series of letters representing
the fixated locations.Cristino et
al. developed a method
(ScanMatch) which consists on
transition matrix among ROIs
and the usage of Levenshtein
distance to compare scan path.
Fixations distribution on predefined
regions of interest
Method Description
ROI visiting Count number of fixations
inside the ROI
ROI revisting Count number of fixations
inside the ROI before first
fixations
Time spent
into ROI
Start time of first fixations
inside ROI minus end time of
last
fixations inside ROI
Distance to
nearest
ROI
Euclidean distance from
fixation centroid to nearest ROI
or
target

13. Regions of interest extraction
By Image Processing
• Privitera and Stark
developed a set image
processing algorithms
(IPAs) to identify ROIs on
a real image: the ten
algorithms mapped the
image into different
domains.
• See the book for a complete
reference.
WTA
• Itti and Koch proposed
the Winner Take All (WTA).
WTA hypothesizes that a
saliency map can be built
from a collection of
separate features map,
representing single visual
features, such as colours or
orientation, across the input

14. Fixations clustering
• Ooms,Andrienko et al. used a visual analytics software
package to analyze the eye movement data for usability
purpose: the area is divided into a set ofVoronoi polygons
which reflected the density of fixations and minimized the
distortion of the scanpath.

15. PATTERN RECOGNITION
ON HUMAN VISION
GiacomoVeneri
Dept. of Applied Neurological Science, University
of Siena
viale Banchi di Sotto 55, 53100, Siena, Italy
Recent Res. Devel. Pattern Rec., 5(2013): 19-47
ISBN: 978-81-7895-584-1

16. 13/07/2013 GiacomoVeneri - http://jugsi.blogspot.com 16
Giacomo Veneri, PhD, MCs
(IT Manager, Human Computer Interaction Scientist)
@venergiac
g.veneri@ieee.org
http://jugsi.blogspot.com
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