A survey on touch dynamics authentication in mobile devices

© 2016 Elsevier Ltd. All rights reserved. There have been research activities in the area of keystroke dynamics biometrics on physical keyboards (desktop computers or conventional mobile phones) undertaken in the past three decades. However, in terms of touch dynamics biometrics on virtual keyboards (modern touchscreen mobile devices), there has been little published work. Particularly, there is a lack of an extensive survey and evaluation of the methodologies adopted in the area. Owing to the widespread use of touchscreen mobile devices, it is necessary for us to examine the techniques and their effectiveness in the domain of touch dynamics biometrics. The aim of this paper is to provide some insights and comparative analysis of the current state of the art in the topic area, including data acquisition protocols, feature data representations, decision making techniques, as well as experimental settings and evaluations. With such a survey, we can gain a better understanding of the current state of the art, thus identifying challenging issues and knowledge gaps for further research

Efficient Verifiable Computation of XOR for Biometric Authentication

This work addresses the security and privacy issues in remotebiometric authentication by proposing an efficient mechanism to verifythe correctness of the outsourced computation in such protocols.In particular, we propose an efficient verifiable computation of XORingencrypted messages using an XOR linear message authenticationcode (MAC) and we employ the proposed scheme to build a biometricauthentication protocol. The proposed authentication protocol is bothsecure and privacy-preserving against malicious (as opposed to honest-but-curious) adversaries. Specifically, the use of the verifiable computation scheme together with an homomorphic encryption protects the privacy of biometric templates against malicious adversaries. Furthermore, in order to achieve unlinkability of authentication attempts, while keeping a low communication overhead, we show how to apply Oblivious RAM and biohashing to our protocol. We also provide a proof of security for the proposed solution. Our simulation results show that the proposed authentication protocol is efficient

A face and speech biometric verification system using a simple Bayesian structure

Identity verification systems that use a mono modal biometric always have to contend with sensor noise and limitations of the feature extractor and matcher, while combining information from different biometrics modalities may well provide higher and more consistent performance levels, However, an intelligent scheme is required to fuse the decisions produced by the individual sensors. This paper presents a decision fusion technique for a bimodal biometric verification system that makes use of facial and speech biometrics. The decision fusion schemes considered have simple Bayesian structures (SBS) that particularize the univariat Gaussian density function, Beta density function or Parzen window density estimation. SBS has advantages in terms Of Computation speed, storage space and its open framework. The performances of SBS is evaluated and compared with that of other classical classification approaches, such as sum rule and Multilayer Perceptron, on a bimodal database

An integrated dual factor authenticator based on the face data and tokenised random number

This paper proposed a novel integrated dual factor authenticator based on iterated inner products between tokenised pseudo random number and the user specific facial feature, which generated from a well known subspace feature extraction technique-Fisher Discriminant Analysis, and hence produce a set of user specific compact code that coined as BioCode. The BioCode highly tolerant of data captures offsets, with same user facial data resulting in highly correlated bitstrings. Moreover, there is no deterministic way to get the user specific code without having both tokenised random data and user facial feature. This would protect us for instance against biometric fabrication by changing the user specific credential, is as simple as changing the token containing the random data. This approach has significant functional advantages over solely biometrics ie. zero EER point and clean separation of the genuine and imposter populations, thereby allowing elimination of FARs without suffering from increased occurrence of FRRs

Iris authentication using privatized advanced correlation filter

his paper proposes a private biometrics formulation which is based on the concealment of random kernel and the iris images to synthesize a minimum average correlation energy (MACE) filter for iris authentication. Specifically, we multiply training images with the user-specific random kernel in frequency domain before biometric filter is created. The objective of the proposed method is to provide private biometrics realization in iris authentication in which biometric template can be reissued once it was compromised. Meanwhile, the proposed method is able to decrease the computational load, due to the filter size reduction. It also improves the authentication rate significantly compare to the advance correlation based approach [5][6] and comparable to the Daugmant's Iris Code [1]

Sorted locally confined non-negative matrix factorization in face verification

In this paper, we propose a face recognition technique based on modification of Non-Negative Matrix Factorization (NMF) technique, which known as Sorted Locally Confined NMF (SLC-NMF). SLC-NMF used NMF to find non negative basis images, subset of them were selected according to a discriminant factor and then processed through a series of image processing operation; to yield a set of ideal locally confined salient feature basis images. SLC-NMF illustrates perfectly local salient feature region which effectively realize "recognition by parts" paradigm for face recognition. The best performance is attained by SLC-NMF compare to the PCA, NMF and local NMF, in FERET Face Database

Eigenspace-based face hashing

We present a novel approach to generating cryptographic keys from biometrics. In our approach, the PCA coefficients of a face image are discretised using a bit-extraction method to n bits. We compare performance results obtained with and without the discretisation procedure applied to several PCA-based methods (including PCA, PCA with weighing coefficients, PCA on Wavelet Subband, and LDA) on a combined face image database. Results show that the discretisation step consistently increases the performance

Generation of replaceable cryptographic keys from dynamic handwritten signatures

In this paper, we present a method for generating cryptographic keys that can be replaced if the keys are compromised and without requiring a template signature to be stored. The replaceability of keys is accomplished using iterative inner product of Goh-Ngo [1] Biohash method, which has the effect of re-projecting the biometric into another subspace defined by user token. We also utilized a modified Chang et al [2] Multi-state Discretization (MSD) method to translate the inner products into binary bit-strings. Our experiments indicate encouraging result especially for skilled and random forgery whereby the equal error rates are < 6.7% and similar to 0% respectively, indicating that the keys generated are sufficiently distinguishable from impostor keys