Mobiles and wearables: owner biometrics and authentication

We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)

Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition

Gesture recognition becomes a popular analytics tool for extracting the characteristics of user movement and enables numerous practical applications in the biometrics field. Despite recent advances in this technique, complex user interaction and the limited amount of data pose serious challenges to existing methods. In this paper, we present a novel approach for hand gesture recognition based on user interaction on mobile devices. We have developed two deep models by integrating Bidirectional Long-Short Term Memory (BiLSTM) network and Bidirectional Gated Recurrent Unit (BiGRU) with Fisher criterion, termed as F-BiLSTM and F-BiGRU respectively. These two Fisher discriminative models can classify user’s gesture effectively by analyzing the corresponding acceleration and angular velocity data of hand motion. In addition, we build a large Mobile Gesture Database (MGD) containing 5547 sequences of 12 gestures. With extensive experiments, we demonstrate the superior performance of the proposed method compared to the state-of-the-art BiLSTM and BiGRU on MGD database and two other benchmark databases (i.e., BUAA mobile gesture and SmartWatch gesture). The source code and MGD database will be made publicly available at https://github.com/bczhangbczhang/Fisher-Discriminant-LSTM

A robustness verification system for mobile phone authentication based on gestures using Linear Discriminant Analysis

This article evaluates an authentication technique for mobiles based on gestures. Users create a remindful identifying gesture to be considered as their in-air signature. This work analyzes a database of 120 gestures of different vulnerability, obtaining an Equal Error Rate (EER) of 9.19% when robustness of gestures is not verified. Most of the errors in this EER come from very simple and easily forgeable gestures that should be discarded at enrollment phase. Therefore, an in-air signature robustness verification system using Linear Discriminant Analysis is proposed to infer automatically whether the gesture is secure or not. Different configurations have been tested obtaining a lowest EER of 4.01% when 45.02% of gestures were discarded, and an optimal compromise of EER of 4.82% when 19.19% of gestures were automatically rejected

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201