Lee Luan Ling

Robustness of multimodal biometric fusion methods against spoof attacks

In this paper, we address the security of multimodal biometric systems when one of the modes is successfully spoofed. We propose two novel fusion schemes that can increase the security of multimodal biometric systems. The first is an extension of the likelihood ratio based fusion scheme and the other uses fuzzy logic. Besides the matching score and sample quality score, our proposed fusion schemes also take into account the intrinsic security of each biometric system being fused. Experimental results have shown that the proposed methods are more robust against spoof attacks when compared with traditional fusion methods.

Biometric access control through numerical keyboards based on keystroke dynamics

This paper presents a new approach for biometric authentication based on keystroke dynamics through numerical keyboards. The input signal is generated in real time when the user enters with target string. Five features were extracted from this input signal (ASCII key code and four keystroke latencies) and four experiments using samples for genuine and impostor users were performed using two pattern classification technics. The best results were achieved by the HMM (EER=3.6%). This new approach brings security improvements to the process of user authentication, as well as it allows to include biometric authentication in mobile devices, such as cell phones.

A hybrid on/off line handwritten signature verification system

This paper proposes a new hybrid handwritten signature verification system where the on-line reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned off-line data. Local foci of attention over the image are determined through a self-adjustable learning process in order to pinpoint the feature extraction process. Both local and global primitives are processed and the decision about the authenticity of the specimen is defined through similarity measurements. The global performance of the system is measured using two different classifiers.

User Authentication through Typing Biometrics Features

This paper uses a static typing biometrics in user authentication. The inputs are the key down and up times and the key ASCII codes captured while the user is typing a string. Four features (key code, two keystroke latencies and key duration) were analyzed, and, seven experiments were performed combining these features. The results of the experiments were evaluated involving three types of user: the legitimate, the impostor and the observer impostor users. The best results were achieved utilizing all features, obtaining a false rejection rate (FRR) of 1.45% and a false acceptance rate (FAR) of 1.89%. This approach can be used to improve the usual login-password authentication when the password is no more a secret. This paper innovates using the combination of four features to authenticate users.

A novel network traffic predictor based on multifractal traffic characteristic

In this work, we propose a network traffic predictor based on a novel multifractal network traffic model. This multifractal traffic model extends the notion of the classical fractional Brownian traffic model proposed by Norros (Queueing Systems, vol.16, p.387-396, 1994), replacing the fractional Brownian motion (fBm) process by the multifractional Brownian motion (mBm) process. The network traffic is assumed to be modeled by the extended fractional Brownian traffic model, which is characterized by its Holder exponents. The value of the Holder exponent at a given time indicates the degree of the traffic burstiness at that time. Based on the mBm covariance structure, a mean-square error discrete-time k-step predictor is implemented. Tests carried out using real wide area network traffic traces proved the proposed traffic model and predictor effectiveness.

A system for automatic extraction of the user-entered data from bankchecks

This paper presents a system for automatic extraction of the user-entered data from Brazilian bankchecks. We have assumed that the layout structure of bankchecks is standardized, that any bankcheck can be identified through the MICR line and that a sample of the background pattern is available for every bankcheck. Based on these assumptions, a template is designed for extracting the user-entered items of any bankcheck, no matter which financial institution has issued it. First, the bankcheck is digitized through a scanner and its skew angle is computed by an algorithm based on the Hough transform. Next a template is generated and used for extracting the user-entered data. The extracted data still shows the presence of the background pattern, character strings, and vertical and horizontal lines. The background pattern is eliminated by a morphological subtraction operation while the baselines are erased using an algorithm based on the projection profiles. The printed character strings are eliminated through a morphological subtraction between the image covered by the signature area and a sample of the character strings generated by the system. Finally, a post-processing algorithm is used for recovering some erased pixels. Experimental results show that this approach is robust and efficient for automatic extracting the user-entered items from Brazilian bankchecks achieving a moderate processing time, very good image quality and excellent accuracy rates.

Offline signature verification system based on the online data

Most of the signature verification work done in the past years focused either on offline or online approaches. In this paper, a different methodology is proposed, where the online reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned offline data. Local windows are built over the image through a self-adjustable learning process and are used to focus on the feature extraction step. The windows positions are determined according to the complexity of the underlying strokes based on the observation of a delta-lognormal handwritten reproduction model. Local features extraction that takes place focused on the windows formed, and it is used in conjunction with the global primitives to feed the classifier. The overall performance of the system is then measured with three different classification schemes.

Analysis of the Multifractal Nature of Speech Signals

Frame duration is an essential parameter to ensure correct application of multifractal signal processing. This paper aims to identify the multifractal nature of speech signals through theoretical study and experimental verification. One important part of this pursuit is to select adequate ranges of frame duration that effectively display evidence of multifractal nature. An overview of multifractal theory is given, including definitions and methods for analyzing and estimating multifractal characteristics and behavior. Based on these methods, we evaluate the utterances from two different Portuguese speech databases by studying their singularity curves (τ(q) and f(α)).We conclude that the frame duration between 50 and 100 ms is more suitable and useful for multifractal speech signal processing in terms of speaker recognition performance [11].

A New Binomial Conservative Multiplicative Cascade Approach for Network Traffic Modeling

In this paper we present a new multifractal approach for modern network traffic modeling. The proposed method is based on a novel construction scheme of conservative multiplicative cascades. We show that the proposed model can faithfully capture some main characteristics (scaling function and moment factor) of multifractal processes. For this new network traffic model we also explicitly derive analytical expressions for the mean and variance of the corresponding network traffic process and show that its autocorrelation function exhibits long-range dependent characteristics. Finally we evaluate the performance of our model by testing both real wired and wireless traffic traces, comparing the obtained results with those provided by other well-known traffic models reported in the literature. We found that the proposed model is simple and capable of accurately representing network traffic traces with multifractal characteristics.

A Multifractal Based Dynamic Bandwidth Allocation Approach for Network Traffic Flows

In this paper, we evaluate a new dynamic bandwidth allocation approach for multifractal traffic arrivals. For this end, first of all we derive an analytical expression for estimating byte loss probability at a single server queue based on the second order statistics for multifractal traffic processes. In order to make the estimation procedure numerically tractable without losing the accuracy, we assume and demonstrate that an exponential model is adequate for representing the variance of traffic processes under different time scale aggregation. Extensive experimental tests validate the efficiency and accuracy of the proposed loss probability estimation approach, its superior performance for admission control and link resource allocation applications with respect to some well-known approaches suggested in the literature.