I'm a PhD student affiliated to the Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD. My research interests
are Computer Vision, Machine Learning, Novelty Detection and Biometrics. My adviser at Hopkins 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 Masters(ECE) and Bachelors(EEE) degrees from Rutgers University, NJ and
University of Peradeniya, Sri Lanka respectivly.
Office : Room Barton Hall 225
Address : ECE, 3400 N. Charles Street, Baltimore, MD 21218.
Deep Transfer Learning for Multiple Class Novelty Detection
We propose a transfer learning-based solution for the problem of multiple class novelty detection. In particular, we propose an end-to-end deep-learning based approach in
which we investigate how the knowledge contained in an external, out-of-distribution dataset can be used to improve the performance of a deep network for visual novelty detection.
Our solution differs from the standard deep classification networks on two accounts. First, we use a novel loss function, membership loss, in addition to the classical
cross-entropy loss for training networks. Secondly, we use the knowledge from the external dataset more effectively to learn globally negative filters, filters that respond
to generic objects outside the known class set.
Conference Version (CVPR '19 )
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
We present a novel model called OCGAN for the classical problem of one-class novelty detection, where, given a set of examples from a particular class, the goal is to determine if a query example is from the same class. Our solution is based on learning latent representations of in-class examples using a denoising auto-encoder network. The key contribution of our work is our proposal to explicitly constrain the latent space to exclusively represent the given class.
Conference Version (CVPR '19 )
Multiple User Active Authentication on 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.
Journal Version (TIFS '18 ).
Conference Version (FG '17 )
Dual-Minimax Probability Machines for One-class Mobile Active Authentication
Due to unavailability of training samples from negative classes, AA
can be viewed as a one-class classification problem. In
this work we introduce a Single-class Minimax Probability
Machine(1-MPM) based solution called Dual Minimax
Probability Machines(DMPM) for AA applications. In this work, we learn an additional hyper-plane to separate
training data from the origin by taking into account maximum
data covariance. Further, we consider the possibility of
modeling the underline distribution of training data as a
collection of sub-distributions.
Conference Version (BTAS '18 )
In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an
output image using a set of unpaired training images. In this paper, we propose an extension of the unsupervised image-to-image
translation problem to multiple input setting. In the paper, we introduce a GAN based framework along witha multi-modal generator
structure and a new loss term, latent consistency loss.
Conference Version (ICPR '18 Best Student Paper )
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.
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 ( TIFS '17).
Conference Version (BTAS '16 ). In Media (CBS news).
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 )
03/02/2019 : Two papers got accepted to be published in CVPR 2019.
11/10/2018 : Journal version of Multi-user AA accepted for publication by TIFS.
10/01/2018 : Paper on one-class AA accepted for BTAS 2018.
08/01/2018 : We recieved ICPR 2018 best student paper award for our work In2I.
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!