https://ubibliorum.ubi.pt/bitstream/10400.6/9176/1/ResultsMICHE2.pdf

Results from MICHE II Mobile Iris CHallenge Evaluation II

An efficient novel approach for iris recognition based on stylometric features and machine learning techniques

https://tandfonline.com/doi/pdf/10.1080/16168658.2019.1611030

Review on Reliable Pattern Recognition with Machine Learning Techniques

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.

Ocular recognition databases and competitions: a survey

The concern over security in all fields has intensified over the years. The prefatory phase of providing security begins with authentication to provide access. Inmany scenarios, this authentication is provided by biometric systems.Moreover, the threat of pandemic hasmade the people to think of hygienic systemswhich are noninvasive. Iris image recognition is one such noninvasive biometric system that can provide automated authentication. Self-organizing map is an artificial neural network which helps in iris image recognition. This network has the ability to learn the input features and perform classification. However, from the literature it is observed that the performance of this classifier has scope for refinement to yield better classification. In this paper, heterogeneous methods are adapted to improve the performance of the classifier for iris image recognition. The heterogeneous methods involve the application of Gravity Search Optimization, Teacher Learning Based Optimization, Whale Optimization and Gray Wolf Optimization in the training process of the self-organizing map classifier. This method was tested on iris images from IIT-Delhi database. The results of the experiment show that the proposed method performs better.

J.M. Martnez-Otzeta

Papers

Iris matching by means of Machine Learning paradigms