# Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations
## 4 Dataset  
In this work, two different sets of data have been used to give evidence of the possibility to use the proposed algorithmic pipeline to associate visual exploratory patterns to biometric information. The first dataset is the publicly available _TabletGaze_ introduced in [58]. The dataset consists of videos recorded by the front camera of a tablet, while 51 subjects, 12 females and 39 males, watched the stimuli (clips) projected on the screen of the device itself held in their hands. The clips watched by the subjects consist of a dot changing its location every 3 s, and the subject was instructed to focus his/her eyes on the dot the whole time. Different videos were acquired by the same subject in this unconstrained mobile setting. In this paper, 4 videos per subject are used, i.e., the 4 videos in which the subjects were in standing position which are the only ones fully containing the face. Videos of 22 of 51 subjects (having ids 1', 2', 3', 4', 5', 6', 9', 11', 12', 15', 17', 19', 23', 27', 29', '37', '39', '40', '44', '49', '50', '51') were taken into consideration. This subset was chosen considering only subjects who maintained at least half face visible for most of the time. Thus, a total of 88 videos were processed. The second dataset was instead expressly acquired for the purposes of this paper. In particular, 17 different subjects (12 men, 5 women) aged in the range of 4-46 years were involved. In particular, there was a kid of preschool age (4 years old) and a school-age kid (9 years old), whereas the remaining subjects were all adults. Each subject was asked to position himself in front of a 27-inch monitor at an approximate distance of about 70 cm, and a webcam recorded his face while 5 short clips of about 20 s each were projected on the screen. The seventeen introduced subjects have been recorded while watching each clip during three different sessions. Each session has been performed with at least 24 h interval. Associated gaze information, extracted by using the pipeline in Section 3, are reported. In addition, other soft biometrics traits regarding the age and gender of the participants have been inserted in the dataset. The dataset consists of video files of the recorded sessions and a command separated values (CSV) structure where each line contains information like age, gender, identity, and temporal gaze tracks of the participant. Figure 2 reports one frame of the dataset extracted by seven participants. Stimuli were selected among those collected in [59]. In particular, the five visual stimuli summed up in Figure 3 were selected. The figure shows their filename as in the original dataset (Available at [[http://www.inb.uni-luebeck.de/tools-demos/gaze](http://www.inb.uni-luebeck.de/tools-demos/gaze)]([http://www.inb.uni-luebeck.de/tools-demos/gaze](http://www.inb.uni-luebeck.de/tools-demos/gaze))). All stimuli consist of high-resolution movies of real-world scenes recorded by a JVC JY-HD10 HDTV video camera. The audio was set off. All clips have a length of about 20 s; their temporal resolution was 29.97 frames per second and their spatial resolution was \(1280\times 720\) pixels (NTSC HDTV progressive scan). All clips were stored to disk in the MPEG-2 video format with a bit rate of 18.3 Mbit/s. The camera was fixed on a tripod. The first three clips contained no camera or zooming movements whereas the sequences depicting animals (bumblebee and doves) contained minor pan and tilt camera motion. The choice of the stimuli was based on the theory that a very low variability, for example using a scene on which all observers follow the same gaze pattern, offers little room to guide the observer's attention; at the same time, a very high variability might indicate the dominance of idiosyncratic viewing strategies that would also be hard to influence.