Complex-valued Iris Recognition Network
Kien Nguyen, Clinton Fookes, Sridha Sridharan, Arun Ross

TL;DR
This paper introduces a fully complex-valued neural network specifically designed for iris recognition, effectively capturing phase and magnitude features crucial for biometric identification, outperforming traditional real-valued methods.
Contribution
The paper presents the first fully complex-valued neural network tailored for iris recognition, enabling automatic complex feature learning and improved extraction of phase and amplitude information.
Findings
Outperforms real-valued networks on benchmark datasets
Effectively captures phase and magnitude features of iris textures
Visualizations show fundamentally different feature extraction
Abstract
In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode; however, the proposed…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
