I'm a PhD student affiliated to the Department of Electrical and Computer Engineering, Rutgers University, NJ. My research interests are Computer Vision, Machine Learning and Biometrics. At present, my adviser at Rutgers is Prof.Vishal Patel. I'm focusing on developing deep learning based machine learning algorithms for Active Authentication and for Biometrics applications in general. I recieved my bachelors degree in Electrical and Computer Engineering from University of Peradeniya, Sri Lanka in 2014.

Contact Information

  • Office : Room 533 CORE building
  • Address : ECE, 94, Brett Road, Piscataway, NJ 08854, USA.
  • Email :
  • Linkedin : phperera

Learning Deep Features for One-Class Classification

We propose a deep learning-based solution for the problem of feature learning in one-class classification. The proposed method operates on top of a Convolutional Neural Network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. We achieve significant improvements over the state-of-the-art in anomaly detection, novelty detection and mobile active authentication tasks. PDF (arXiv). Code (Github).

Efficient and Low Latency Detection of Intruders in Mobile Active Authentication

We address the problem of quickly detecting intrusions with lower false detection rates in mobile AA systems with higher resource efficiency. Bayesian and Minimax versions of the Quickest Change Detection (QCD) algorithms are introduced to quickly detect intrusions in mobile AA systems. These algorithms are extended with an update rule to facilitate low frequency sensing which leads to low utilization of resources. Journal Version ( Accepted for TIFS ). Conference Version (BTAS '16 ). In Media (CBS news).

Towards Multiple User Active Authentication in Mobile Devices

With the advent of mobile devices a mobile device may be accessed by more than a single enrolled user. In this context, verification of multiple enrolled users has a practical importance. We address the issue of performance degradation associated with multiple user authentication as compared to single user authentication. We introduce the notion of probability of negativity to alleviate the effect of multiple users in authentication. We further introduce a simple fusion scheme with the existing authentication methods to increase the intruder detection accuracy. Conference Version (FG '17 )

Extreme Value Analysis for Mobile Active User Authentication

In this work, we propose to improve the performance of mobile Active Authentication (AA) systems in the low false alarm region using the statistical Extreme Value Theory (EVT). The problem is studied under a Bayesian framework where extremal observations that contribute to mis-verification are given more prominence. We propose modeling the tail of the match distribution using a Generalized Pareto Distribution (GPD) in order to make better inferences about the extremal observations. A method based on the mean excess function is introduced for parameter estimation of the GPD. Conference Version (FG '17 )


  • 12/10/2017 : Our work on resource efficient AA has been accepted for publication at TIFS.
  • 07/08/2017 : Our work on Active Authentication appears on CBS news!
  • 01/23/2017 : Two papers of mine have been accepted for FG 2017!
  • 09/08/2016 : Presented my paper on Quickest Intrusion Detection at BTAS 2016.
  • 05/06/2016 : Presenting my work on Active Authentication at MACV 2016 at John Hopkins.
  • 15/04/2016 : Msy work on Quickest Intrusion Detection on AA has been accepted for an oral in BTAS '16.