One shot learning approach for cross spectrum periocular verification.

The use of face mask during the COVID-19 pandemic has increased the popularity of the periocular biometrics in surveillance applications. Despite of the rapid advancements in this area, matching images over cross spectrum is still a challenging problem. Reason may be two-fold 1) variations in image illumination 2) small size of available data sets and/or class imbalance problem.

A novel periocular biometrics solution for authentication during Covid-19 pandemic situation.

The outbreak of novel coronavirus in 2019 has shaken the whole world and it quickly evolved as a global pandemic, placing everyone in a panic situation. Considering its long-term effects on day to day lives, the necessity of wearing face mask and social distancing brings in picture the requirement of a contact less biometric system for all future authentication systems. One of the solutions is to use periocular biometric as it does not need physical contact like fingerprint biometric and is able to identify even people wearing face masks.

FraudMiner: a novel credit card fraud detection model based on frequent itemset mining.

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly.