Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

Abstract

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.

8 pages, 12 figures, 6 tables. Best poster award at BTAS 2016

References in corpus (4)

• Learning Face Representation from Scratch
• Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
• Partial Face Detection for Continuous Authentication
• Face Alignment by Local Deep Descriptor Regression

Cited by in corpus (18)

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• One-Class Convolutional Neural Network
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• Smartphone Multi-modal Biometric Authentication: Database and Evaluation
• Promises and Challenges in Continuous Tracking Utilizing Amino Acids in Skin Secretions for Active Multi-Factor Biometric Authentication for Cybersecurity
• MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns
• BeCAPTCHA: Detecting Human Behavior in Smartphone Interaction using Multiple Inbuilt Sensors
• Swipe dynamics as a means of authentication: results from a Bayesian unsupervised approach
• Continuous Authentication of Smartphones Based on Application Usage
• Pooling Facial Segments to Face: The Shallow and Deep Ends
• Location-based Behavioral Authentication Using GPS Distance Coherence
• The Mobile AR Sensor Logger for Android and iOS Devices
• End-to-end User Recognition using Touchscreen Biometrics
• You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
• Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
• MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
• A Closer Look at Mobile App Usage as a Persistent Biometric: A Small Case Study