640 citations · 3.8k across the 145 of their papers we have counts for
8 papers · 1 filter
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
Upal Mahbub, Sayantan Sarkar, Vishal M. Patel +1
In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02…
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan +5
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.…
Partial Face Detection for Continuous Authentication
Upal Mahbub, Vishal M. Patel, Deepak Chandra +2
In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped an…
Optimized Kernel-based Projection Space of Riemannian Manifolds
Azadeh Alavi, Vishal M Patel, Rama Chellappa
It is proven that encoding images and videos through Symmetric Positive Definite (SPD) matrices, and considering the Riemannian geometry of the resulting space, can lead to increas…
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
Rajeev Ranjan, Vishal M. Patel, Rama Chellappa
We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN). The proposed…
Deep Feature-based Face Detection on Mobile Devices
Sayantan Sarkar, Vishal M. Patel, Rama Chellappa
We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front facing camera. The proposed method is able to detect faces in images co…
Face Alignment by Local Deep Descriptor Regression
Amit Kumar, Rajeev Ranjan, Vishal Patel +1
We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing deep CNNs, our model computes local features aroun…
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
Rama Chellappa, Jun-Cheng Chen, Rajeev Ranjan +4
Over many decades, researchers working in object recognition have longed for an end-to-end automated system that will simply accept 2D or 3D image or videos as inputs and output th…