Department of Electronics, University of Kent, Canterbury, Kent CT2 7NT, UK ... cations) to improve identity prediction accuracy of signature-based tasks.
Improving Identity Prediction in Signature-based Unimodal Systems Using Soft Biometrics M´ arjory Abreu and Michael Fairhurst Department of Electronics, University of Kent, Canterbury, Kent CT2 7NT, UK {mcda2,M.C.Fairhurst}@kent.ac.uk
Abstract. System optimisation, where even small individual system performance gains can often have a significant impact on applicability and viability of biometric solutions, is an important practical issue. This paper analyses two different techniques for using soft biometric information (which is often already available or easily obtainable in many applications) to improve identity prediction accuracy of signature-based tasks. It is shown that such a strategy can improve performance of unimodal systems, supporting high usability profiles and low-cost processing.
1
Introduction
Biometrics-based systems for individual identity authentication are now well established [1]. Many factors will influence the design of a biometric system and, in addition to obvious performance indicators such as accuracy rates, issues concerning reliability, flexibility, security and usability are extremely important. It is necessary to understand and evaluate each part of the process in developing these systems. The advantages and disadvantages of the many different modalities available (fingerprint, face, iris, voice, handwritten signature, etc) are well documented, and a wide variety of different classification/matching techniques have been extensively explored. There are many proposed approaches which use multimodal solutions to improve accuracy, flexibility and security, though these solutions can be of relatively high cost and diminished usability [2]. Thus, where possible unimodal solutions are still important, and, indeed, attempts to improve the performance of such configurations are widely reported. One approach which aims to improve the accuracy of unimodal systems is to include non-biometric (soft biometric) information in the decision-making process. In the main, the reported work in this area incorporates relevant information (such as gender, age, handedness, etc) in order to help to narrow the identityrelated search space [3], [4], [5], [6], [7], [8], but the precise methodology adopted is sometimes quite arbitrary. This paper presents an investigation of two rather different but potentially very effective techniques for including soft biometric information into the overall identification decision. Although we will focus in this paper specifically on the J. Fierrez et al. (Eds.): BioID MultiComm2009, LNCS 5707, pp. 348–356, 2009. c Springer-Verlag Berlin Heidelberg 2009
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handwritten signature as the biometric modality of choice, only one of our proposed approaches is modality-dependent, giving a valuable degree of generality to our study.
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Techniques for Exploiting Soft Biometrics
The available literature shows a relatively modest number of examples of using soft biometrics to improve accuracy yet, when this approach is adopted, it generally leads to improved performance. In this paper we propose two different ways of using such information in a more efficient way than has been adopted hitherto. 2.1
Soft Biometrics as a Tool for Feature Selection
In this approach, the soft biometric information works as a feature selector, the selection being related to the demographic information that is saved in a Knowledge Database of the system. Fig. 1 shows how the process is realised. During the training phase, it is important to understand the relationship between the dynamic features and the soft biometric information, from which the system is able to choose the most suitable features for each user. This is the information that will be stored in the Knowledge Database module and will be used in the feature selection. The feature analysis is carried out, after training the system with all the features, as follows: 1. Select all the static features and test the system saving the partitioned error rates related with the soft biometrics. 2. For each dynamic feature: – Add this feature to the vector of the static features, – Test this new vector in the system, – Save the error rates to each related soft biometric. 3. Once the system is tested with all different feature combinations and the partitioned error rates saved, the analysis of each dynamic feature is executed with respect to each soft biometric information: – Save in the Knowledge Database module each dynamic feature that generated better performances than when using only static features. As an example to illustrate the general operation of this method, a hypothetical three-feature (fea1, fea2 and fea3) biometric-based identity prediction task is considered. In this task let us assume that the features fea1 and fea2 are static features and fea3 is a dynamic feature. The soft-biometric information of the user is known and is recorded as either “X” or “Y” (these labels representing appropriate values depending on the particular instance of soft biometric information. For instance, for gender the labels will be “male” and “female”). After the training phase with all the features (fea1, fea2 and fea3), the system is tested with fea1 and fea2 (only static features) first. The partitioned error rates for each “X” and “Y” for the soft-biometric information might then appear as
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Table 1. Example: Fictional illustrative data Static Features System Soft-Biometric Error Mean “X” “Y” 10.21% 5.40% 4.81% Static Features + Dynamic Feature System Soft-Biometric Error Mean “X” “Y” 9.54% 4.54% 5.00%
in Table 1. The next step is to test the system with fea1, fea2 and fea3 and also save the partitioned error rates. This information can also be seen in Table 1. If there is any gain in accuracy with respect to the partitioned error rates, then this dynamic feature is seen to improve performance. The information stored in the Knowledge Database module (Fig. 1) during the training phase is as follows: – if soft-biometric information is tagged “X” then fea3 can improve accuracy. – if soft-biometric information is tagged “Y” then fea3 is not contributing to improved accuracy. Once the features are selected, the corresponding input values are shown to the system and the system computes its result.
Fig. 1. Soft Biometric as feature selector
2.2
Fig. 2. Soft Biometric as input features
Soft Biometric as an Extra Input Feature
In this approach, the soft biometric information functions, in essence, as an extra input feature. The soft biometric information is used in the same way as any other biometric feature and is simply added to the input vector. Fig. 2 shows a schematic illustration of this process. This information can be added into our analysis of system performance, where these additional characteristics effectively define sub-groups within our overall
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test population. These new information sources contribute to the system input information in the same way as the extracted sample features are used, which requires the integration of the further features. Using the same example introduced in Section 2.1, the input of the system will be the three features (fea1, fea2 and fea3) and the additional soft biometric feature, designated soft-fea here. In this case, the input will be a binary value, 10 for “X” and 01 for “Y”, using Hamming Distance coding [9].
3
A Case Study
In order to analyse how these two approaches compare in an application based on real data, we now present a practical Case Study. 3.1
Database
The multimodal database used in this work was collected in the Department of Electronics at the University of Kent [10] as part of the Europe-wide BioSecure Project. In this database, there are samples of Face, Speech, Signature, Fingerprint, Hand Geometry and Iris biometrics of 79 users collected in two sessions. In the work reported here we have used the signature samples from both sessions. Table 2. Signature features Feature Execution Time Pen Lift Signature Width Signature Height Height to Width Ratio Average Horizontal Pen Velocity in X Average Horizontal Pen Velocity in Y Vertical Midpoint Pen Crossings Azimuth Altitude Pressure Number of points comprising the image Sum of horizontal coordinate values Sum of vertical coordinate values Horizontal centralness Vertical centralness
Type Dynamic Dynamic Static Static Static Dynamic Dynamic Dynamic Dynamic Dynamic Dynamic Static Static Static Static Static
The database contains 25 signature samples for each subject, where 15 are samples of the subject’s true signature and 10 are attempts to imitate another user’s signature. In this investigation we have used only the 15 genuine signatures of each subject. The data were collected using an A4-sized graphics tablet with a density of 500 lines per inch. There are 16 representative biometric features extracted from each signature sample, as identified in Table 2. These features are chosen to be representative of those known to be commonly adopted in signature processing applications. All the available biometric features are used in the classification process as input to the system. During the acquisition of this database, the subjects were required to provide some additional information which constitutes exactly the sort of soft biometric data discussed above.
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In particular, we recorded the following information about each user: – Gender: Male or Female. – Age information: For our purposes three age groups were identified, namely: under 25 years, 25-60 years and over 60 years. – Handedness: Right or Left. In the study reported here, we have explored the users’ characteristics with respect to the age information and handedness information. The possible values assigned to the age features, use a Hamming Distance coding [9], and are defined as 100 (< 25), 010 (25-60) or 001 (> 60), depending on the age group. In the same way, the possible values assigned to handedness features are 10 (right) and 01 (left). 3.2
Classification Methods
In order to evaluate the effectiveness of the proposed two approaches, we choose four different classifiers (to allow a comparative study), as described below. – Fuzzy Multi-Layer Perceptron (FMLP) [11]: This classifier incorporates fuzzy set theory into a multi-layer Perceptron framework, and results from the direct “fuzzyfication” in the network level of the MLP, in the learning level, or in both. – Support Vector Machines (SVM) [12]: This approach embodies a functionality very different from that of more traditional classification methods and is based on an induction method which minimizes the upper limit of the generalization error related to uniform convergence. – K-Nearest Neighbours (KNN) [13]: In this method, the training set is seen as composed of n-dimensional vectors and each element represents an n-dimensional space point. The classifier estimates the k nearest neighbours in the whole dataset based on an appropriate distance metric (Euclidian distance in the simplest case). – Optimized IREP (Incremental Reduced Error Pruning) (JRip) [14]: The Decision Tree usually uses pruning techniques to decrease the error rates of a dataset with noise, one approach to which is the Reduced Error Pruning method. In order to guarantee robustness in the classification process, we chose a tenfold-cross-validation approach because of its relative simplicity, and because it has been shown to be statistically sound in evaluating the performance of classification tasks [15]. In ten fold cross validation, the training set is equally divided into ten different subsets. Nine out of ten of the training subsets are used to train the classifier and the tenth subset is used as the test set. The procedure is repeated ten times, with a different subset being used as the test set.
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Experimental Results
In order to analyse the performance of our proposed new techniques for using soft biometric information to improve accuracy, it is important first to evaluate the performance of the classifiers without this additional information. Table 3 shows the error rates of the classifiers when all features (All-F) are used and when only static features (Sta-F) are used. The classifier that presents the best results, highlighted in bold in Table 3, is FMLP. It is important to note that in all the classifiers, using all features leads to an improvement of around 5%, (corresponding to approximately 60 additional samples that are classified correctly). This is perhaps not in itself surprising, but confirms what can readily be determined from the available literature, that using dynamic features improves the accuracy of a signature-based classification task. Table 3. Results without and with soft-biometrics Classification Results without Soft biometric Methods All-F FMLP 10.21%±2.37 Jrip 11.67%±2.96 SVM 10.92%±2.31 KNN 14.12%±2.31 Classification Results without Soft biometric Methods Sta-F FMLP 15.97%±3.69 Jrip 18.22%±3.54 SVM 16.31%±3.97 KNN 22.84%±4.01
Age-based results S-F/Age All-F+Age Sta-F+Age 8.84%±1.98 9.54%±2.91 12.91%±2.57 11.87%±1.74 10.28%±2.57 16.84%±2.91 8.69%±1.67 8.21%±2.64 13.73%±2.54 15.34%±1.39 12.47%±2.83 18.81%±2.88 Handedness-based results S-F/Hand All-F+Hand Sta-F+Hand 10.27%±2.89 10.81%±3.69 12.09%±1.58 12.64%±2.81 11.27%±3.47 15.74%±1.79 9.57%±2.36 9.83%±3.59 11.94%±1.67 14.37%±2.47 13.97%±3.77 16.33%±1.88
However, Table 3 shows the results for the same classifiers when the additional soft biometric information (Age and Handedness) is also incorporated. The three columns show respectively the error rate measured in the cases for selected features using soft biometrics (S-F), all features plus soft biometrics (All-F) and static features plus soft biometrics (Sta-F). From an analysis of Table 3, it is possible to see that by adding selected dynamic features to the static features or adding soft biometrics either to all features or only to static features produces a better performance than using only static features. The overall error rates are broadly related to the sophistication (and, generally therefore, complexity) of the classifiers, with the best results being obtained with the SVM and FMLP classifiers. Analysing the results based on the incorporation of the age-based additional information and using the t-test, it is possible to note that there is no statistical difference between the S-F/Age results and the All-F+Age results while, on the other hand, these results are both statistically better than the Sta-F+Age. Analysing the results when the handedness information is incorporated and using the t-test, it can be seen that all the results are statistically comparable. Fig. 3 and Fig. 4 show the comparison among all five different configurations according to the partitioned age bands and left and right handedness. Careful
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Fig. 3. Result of all classifiers in all config- Fig. 4. Result of all classifiers in all configurations partitioned into age bands urations partitioned into handedness
examination of these results shows, however, how choice of classifier and configuration can lead directly to an optimised process with respect to a given task objective (for example, when working within a particular age band, or focusing on a particular population group). 3.4
Enhancing Off-Line Processing
An issue of particular interest here is related to a question about off-line signature processing and how this might be enhanced. Most implementations of signature verification systems use a wide range of feature types, but almost all assume the availability of the dynamic features which are only extractable when on-line sample acquisition is possible. Yet there are still important application where only off-line sampling is possible (remote bank cheque clearing, many document-based applications, etc), and thus it is instructive to consider performance achievable when only static features can be used for processing. It is generally expected that static-only solutions will return poorer levels of performance than can be achieved with the much richer feature set available when dynamic features are incorporated and, indeed, the results shown above confirm this here. It is interesting to consider the results of enhancing the processing by incorporating soft biometrics, such as can be seen when we add in the age-based information, with the results shown in Fig. 3 and Fig. 4. The improvement in performance which this brings about suggests a very valuable further benefit of an approach which seeks to exploit the availability of soft biometrics as a means of enhancing performance. In this case, such an approach provides the opportunity for significant enhancement in a scenario which has important practical implications yet which is often especially limited by the inherent nature of the task domain.
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Concluding Remarks
This paper has introduced two new techniques by means of which to include soft biometric information to improve identity prediction accuracy. The results presented are very encouraging, and show how additional information often available or explicitly collected in practical scenarios can be exploited in a way which can enhance the identification process. Although accuracy improvements tend to be modest (perhaps not surprisingly given the small scale of this initial experimental study) the gains afforded can nevertheless make an impact in practical situations and provide a basis for further development of the general strategy proposed. Further work is required to develop optimisation procedures in configurations such as those investigated here, and to extend the analysis to integrate different types of soft biometric information. Already, however, the work reported here is beginning to offer some options to a system designer in seeking to improve error rate performance in unimodal systems, providing alternatives to the increased complexity and reduced usability incurred in multibiometric systems.
Acknowledgment The authors gratefully acknowledge the financial support given to Mrs Abreu from CAPES (Brazilian Funding Agency) under grant BEX 4903-06-4.
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