INFORMATION FUSION IN BIOMETRICS: A CASE STUDY IN ONLINE SIGNATURE. Waheeda Almayyan

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1 INFORMATION FUSION IN BIOMETRICS: A CASE STUDY IN ONLINE SIGNATURE Waheeda Almayyan De Montfort University The Gateway, Leicester LE1 9BH, England, UK walmayyan@dmu.ac.uk Hala S. Own Department of Solar and Space Research, National Research Institute of Astronomy and Geophysics Helwan, Cairo 11421, Egypt halaown@gmail.com Hussein Zedan De Montfort University The Gateway, Leicester LE1 9BH, England, UK hzedan@dmu.ac.uk ABSTRACT Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. A biometric system is essentially a pattern recognition system which makes a personal identification decision by determining the authority of specific physiological or behavioral traits. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and undesirable error rates. Some of the restrictions can be lifted by designing a multimodal biometric system. Multimodal biometric systems are those which utilize, or are capable of utilizing, more than one physiological or behavioral characteristic for enrollment either in verification or identification mode. Among the biometric traits, handwritten signature is considered to be the most widely accepted biometric for identity verification. Researches directions in signature from an information fusion perspective are outlined in this paper. Keywords: Biometrics, Multimodal Biometrics, Fusion, Signature Authentication, Signature verification. 1. INTRODUCTION The term biometric is derived from the Greek words Bió-metŕiks, Bió which means life and metŕiks means measures. So biometric is the measurement and statistical analysis of unchanging biological characteristics. Biometrics measure an individual s unique physical or behavioral traits to authenticate their identity. Common physical traits include fingerprints, ear, hand or palm geometry and retina, iris or facial characteristics. Behavioral traits include voice, signature, keystroke pattern and gait. Biometrics-based personal authentication systems have gained intensive research interest due to the unreliability and inconvenience of traditional systems [1]. While looking for a proper biometric to be used in a particular application, the distinguishing traits should possess the following properties: uniqueness, stability, collectability, performance, acceptability and forge resistance [3]. Due to the unreliability and inconvenience of traditional methods, such as passwords, pin numbers, key cards and smart cards, the biometric identification is more preferred. Therefore, biometrics is considered to be a secure and convenient authentication tool since it can t be borrowed, stolen or even forgotten.

2 Signature verification, as a method for lowersecurity authentication, has gained an acceptable reputation throughout human civilization, although one s signature may change overtime and it s not nearly as unique or difficult to simulate, like iris pattern or fingerprints. The objective of a signature verification process is to discriminate between two classes genuine and forged signatures, which is not an easy task bearing in mind that finding two identical signatures from the same writer has never happened, and such variation is called intra-personal variation. Most biometric systems that are currently in use typically employ a single biometric trait; such systems are called uni-biometric systems. Despite considerable advances in recent years, there are still challenges that negatively influence their resulting performance, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the restrictions can be lifted by designing a multimodal biometric system [2]. This paper presents an investigation into the research trends on multimodal fusion process. This paper is organized as follows. Section 2 gives an overview of fusion in biometrics. Section 3 gives a brief overview of signature as a behavioral biometric, and section 4 outlines the new directions in signature from a multimodal point of view. 2. FUSION IN BIOMETRICS Although some biometrics has gained more acceptance then others, nevertheless, each biometric has its strengths and limitations, and no single biometric is expected to meet the desired performance of the authentication applications. Multibiometric systems develop the fusion of two or more uni-modal biometric systems. Such systems are expected to be more reliable due to the integration of information obtained from a variety of independent modalities [3]. The integration approach to improve performance can take any number of different forms. e.g., Fig.1 illustrates multiple choices for integration in an online signature based system. Any multibiometric system can be based on one or a combination of the following fusion scenarios [2]: Multiple modalities, the biometric traits are extracted from two or multiple biometric modalities using single or multiple sensors. This is also known as multimodal biometrics. For example, a biometric recognition system based on combining face and offline signature attributes would be considered a multimodal system regardless of whether both images were captured by different or same imaging device. Multiple sensors, the same instance of biometric trait are acquired by different sensors. For example, a face recognition system might use multiple cameras for creating a 3D image. Multiple algorithms, a single sample captured by a single sensor is processed by two or more different algorithms. One example is processing fingerprint images according to minutiae- and texture-based representations. Multiple instances, number of biometric samples from different instances of the same biometric characteristics are used in this system. An example of multiple instances is using left and right iris images for identity authentication. Repeated instances, the same biometric modality and instance is acquired with the same sensor several times. One example is capturing sequential frames of facial images. 3. SIGNATURE AS A BIOMETRIC Signature verification can be classified generally as online or offline according to the acquisition technique. Offline signature verification takes a 2D static image record of the signature, thus it is useful in automatic signature verification found on bank checks and documents authentication. Offline verification techniques are based on limited information available exclusively from shape and structural characteristics of the signature image. Whereas, online signature verification track down trajectory and other time-variable sequence variables using specially designed tablets or other devices during the act of signing. So it is more required to be used in real-time applications, such as financial transactions, automatic document authenticity and office automation. As a result of the development in the technology of data acquisition, online signature verification systems have become more reliable swift and accurate more than their counterpart techniques. Most approaches are based on measuring the movement of the pen on a digitized tablet. Some tablets also measure pressure on the pen tip and several angles such as tilt or rotation. 4. A CASE STUDY IN SIGNATURE Although the problem of automatic signature verification has been extensively studied, it is nevertheless, not a fully solved problem [6]. The fusion of multiple sources of information has proven that it will improve the performance of the biometric system, and there are multiple choices for the integration (figure 1).

3 Multiple sensors (e.g., optical and capacitance sensors) Multiple matchers (e.g., HMM and Distancebased matchers) SIGNATURE BASED SYSTEM 4.1 Multiple Sensor System In this early stage of fusion, the raw data, derived from the same biometric characteristic with two or more sensors, is combined. An example of the sensor fusion level is capturing a online signature signal simultaneously with two different tablets. Although fusion at primitive stages is expected to improve the recognition accuracy, it is not applicable with incompatible data gathered from different modalities. The fusion at the sensor level was a matter of research interests in fingerprint and face recognition. For example constructing a composite fingerprint or face template using multiple impressions or 2D snapshots with the same sensor or camera is called Image mosaicking or mosaicing. The performance of online signature verification two different commercial Tablet PCs were evaluated in [7]. Authentication performance experiments were reported considering both random and skilled forgeries by using a database with over 3000 signatures, where considerable difference in the performance of the two tablets was noticed. Future work may include interoperability experiments or multi-sensor experiments using more than one tablet interchangeably. 4.2 Multiple Matcher System Multiple biometrics (e.g., signature, voice) Figure1. Multiple choices for integration in online signature based system. In the last years, different multi-expert (ME) approaches have been investigated to improve signature verification performance. They can combine decisions obtained through multiple representations and matching algorithms at both local and global levels. Such ME verification system were based on feature sets, global and local strategies, parameter features and function features, static and dynamic features. Several decision combination schemes have been employed, such as majority voting, Borda count, Dempster Shafer evidence theory, simple and weighted averaging and NNs. 4.3 Multiple biometric fusion Multimodal fusion refers to the combination of multiple biometric clues of the same individual. Multimodal systems offer the potential of improving the overall authentication performance by combining different modalities. Several related works have shown that using signature with other modalities permits significant improvements the overall accuracy. A multimodal biometric system was developed based on the biometrics of face, ear and signature were combined in [11] based on Principal Component Analysis (PCA) and Fisher s Linear Discriminant (FLD) methods. The ranks of individual matchers were combined using the Borda count method and the Logistic regression method. The results indicated that fusing individual modalities improved the overall performance of the biometric system. A system used utilized face and signature modalities is proposed in [10].The proposed system is designed for such applications where the training database contains minimum number (one each) of face and signature images. In face recognition unit, first the face is detected using the triangulation algorithm and then it is recognized based on KDDA and the Haar wavelet algorithm. In signature recognition unit, the signature is matched with stored database image using the Haar wavelet. Multibiometrics algorithm considers the results of face and signature recognition and gives the final matching result based on the fusion rule. This system is tested on a database prepared by the authors and the overall accuracy of the system is found to be 94.37%. Another multimodal biometric system studied the performance of speech and signature features combined [13]. Speaker recognition system was built using Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Vector Quantization (VQ) for modeling. An offline signature recognition system is also built using Vertical and Horizontal Projection Profiles (VPP and HPP) and Discrete Cosine Transform (DCT) for feature extraction. A multimodal biometric system is demonstrated using score level fusion of speaker and signature recognition systems. Sum rule was used for the fusion of the biometric scores. Experimental results showed the efficacy of the suggested multimodal biometric system even when the biometric data is affected by noise. A similar result was obtained with combing face with the other mentioned biometrics [12]. In [14], a comparison of trained rules and fixed rules is presented. In that study, the effectiveness of Arithmetic Mean Rule (AMR) and SVM are compared in a multimodal biometric score fusion based on the integration of the scores for speech and online signature

4 modalities in two different experimental conditions. It is shown that in the first case, clean speech and clean online signature data, AMR with Min-Max gives the best performance, while in the second (noisy) case, with degraded speech data and clean online signatures, SVM gives performance equivalent to those obtained with AMR after score range-normalization via a Bayes normalization [14]. Such results are much better than those given by the AMR with Min-Max and Tanh Estimator. For the fusion by AMR, three score rangenormalization methods are used, Min-Max, Tanh Estimator and Bayes normalization. For the SVM no range-normalization is used. In [8] a multi-modal user authentication system is implemented on a PDA is described as part of the SecurePhone project which will permit documents to be interactively modified and agreed in a mobile environment. Three modalities were chosen: voice, face and signature. These modalities were chosen because they are easy to acquire on a standard PDA and are all characterized by a high user acceptance. Several fusion techniques were tested for biometric evidence combination. Best performance achieved for voice, face, signature and fused modalities was 2.3, 17.3, 4.3 and 0.6% EER for BANCA/BIOMET and 3.2, 27.6, 8.0 and 0.8% EER for the PDA database. A multi-modal biometrics authentication system based on voice pitch and on-line signature verification is proposed in [16]. The on-line signature verification based on DWT sub-band decomposition, while the voice pitch was extracted by a self-correlation function and verified by a standardized variable. Experiments carried out indicated that the verification rate of 96.7% was obtained by the series system of abstract level fusion, and it was improved by 0.6% than the signature verification rate. On the other hand, verification rate of 97.5% was obtained by the score level fusion, and it was improved by 1.5% than the signature verification rate. In the multimodal approach presented in [5], the biometrics voice and online signature are fused with one another. While the matching algorithm for the speaker identification modality is based on a single Gaussian Mixture Model (GMM) algorithm, the signature verification strategy is based on four different distance measurement functions, combined by multialgorithmic fusion at the matching score level. Results showed that by exploitation of a multialgorithmic signature verification system, an increase in recognition accuracy of 15% was accomplished. The combination of on-line signature and voice biometrics has been studied in [15] to insure secure access to patient records. Signatures were verified using the dynamic time warping technique. Voice was verified using a commercial, off the shelf, software development kit. After normalization of scores, fusion was performed at the matching score level using the weighted sum rule. A prototype bimodal authentication system for accessing medical records has been designed and evaluated on a small multimodal database of 50 users, resulting in an average equal error rate (EER) of 0.86%. 5. SUMMERY Lately, along with the increasing security demands, the field of Biometrics has attracted more attentions. Up to date results achieved in international competitions using benchmark databases have confirmed that signature authentication systems can have an accuracy level similar to those achieved by other biometric systems [4, 6]. Signature verification is a very attractive field of research from both scientific and commercial points of view. In spite of this attention, there are plenty of challenges and more work needs to be done in this area. In this paper, we present the researches which have approached from an information fusion perspective. 6. REFERENCES [1] K. 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