Research, implementation and comparison between methods for pupil detection in an image

The objective of this work is to design and implement a pupil detection solution to be used as part of an anti-spoofing mechanism in face recognition. This is intended to improve the security and reliability of face recognition technology by minimizing its risks and, thus, provide confidence to users and companies that employ it. In addition, the project wants to focus on the efficiency and accuracy of the final solution, to do so, various alternatives based on different methods will be developed and compared. To achieve this, the project follows a linear work methodology, that is to say, starting from the search of information, through the theoretical design phase, the code implementation and ending with the presentation of an operational solution. The results achieved after the whole process have been satisfactory and show the viability of the target set. Also, the work provides a series of possible future improvements while reflecting how the initial idea has been evolving and maturing to end up resulting in a product more solid and adjusted to the detected need

PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research

The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10–2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT