The use of intra-palm propagation signals as biometrics is proposed. The intra-palm propagation signal is an electromagnetic wave propagated in the palm. In this study, intra-palm propagation signals are measured using dedicated measuring devices and their verification performance based on the Support Vector Machine is evaluated using twenty-one subjects. The equal error rate is approximately 25 %

Inada, Takashi

Li, Shigang

Nakanishi, Isao

Sodani, Yuta

Tottori University Research Result Repository

鳥取大学研究成果リポジトリTottori University research result repositoryタイトルTitlePerformance Evaluation of Intra-Palm PropagationSignals as Biometrics著者Auther(s)Nakanishi, Isao; Inada, Takashi; Sodani, Yuta; Li,Shigang掲載誌・巻号・ページCitationProceedings of 2013 International Conference onBiometrics and Kansei Engineering (ICBAKE2013) : 91 -94刊行日Issue Date2013資源タイプResource Type会議資料 / Conference Paper版区分Resource Version著者版 / Author権利Rights© © 2013 IEEE. Personal use of this material ispermitted. Permission from IEEE must be obtainedfor all other uses, in any current or future media,including reprinting/republishing this materialfor advertising or promotional purposes, creatingnew collective works, for resale or redistributionto servers or lists, or reuse of any copyrightedcomponent of this work in other works.DOI 10.1109/ICBAKE.2013.17URL http://repository.lib.tottori-u.ac.jp/5605Performance Evaluation of Intra-Palm Propagation Signals as BiometricsIsao Nakanishi, Takashi Inada, Yuuta Sodani, and Shigang LiGraduate School of EngineeringTottori University4-101 Koyama-minami, Tottori, 680-8552 JapanEmail: fnakanishi,lig@ele.tottori-u.ac.jpAbstract—The use of intra-palm propagation signals asbiometrics is proposed. The intra-palm propagation signal isan electromagnetic wave propagated in the palm. In this study,intra-palm propagation signals are measured using dedicatedmeasuring devices and their veriﬁcation performance basedon the Support Vector Machine is evaluated using twenty-onesubjects. The equal error rate is approximately 25 %.Keywords-biometrics; intra-palm propagation signal; SVMI. INTRODUCTIONAlmost all of biometric modalities are studied as analternative to a conventional authentication method such asthe password or the ID card. However, there are applicationsin which the conventional method is unusable.One of the applications is user management, in whichsuccessive authentication such as continuous authentication[1] or on-demand authentication [2] is required. Users arerequired to present their biometric data successively; there-fore, the password and the ID card are inapplicable. In suchan application, only the biometrics is applicable. However,not all biometric modalities are usable. Only the modalitiesthat make it possible to present biometric data transparentlyare applicable.Considering the situation of user management, it is imag-inable that users are controlling a system while gripingor touching something of the system such as a handle ofa vehicle or a mouse device of a computer. In such acase, palms are an interface between the system and theusers. Thus, we have proposed to use intra-palm propagationsignals as transparent biometrics [3].We conﬁrmed that intra-palm propagation signals weremeasurable by using the proposed measuring system andsurely propagated in palms. However, the veriﬁcation per-formance based on the Euclidian distance was very low. Inthis study, we introduce the support vector machine (SVM)into the veriﬁcation stage in order to improve the veriﬁcationperformance. The effectiveness of the SVM had alreadyconﬁrmed in Ref. [4].In Sect. II, we explain about the measurement of intra-palm propagation signals. Next, we introduce the feature ex-traction and veriﬁcation method in Sect. III. The veriﬁcationis performed based on the SVM. In Sect. IV, the veriﬁcationperformance of intra-palm propagation signals is evaluatedSignal GeneratorDigital Oscilloscope++-Electromagnetic Wave-Figure 1. The measuring system of intra-palm propagation signals.in the experiments using twenty-one subjects. Finally, theconcluding remarks are presented in Sect. V.II. MEASUREMENT OF INTRA-PALM PROPAGATIONSIGNALSA. Intra-Palm Propagation SignalFigure 1 shows a basic structure of the measuring systemof intra-palm propagation signals. It is based on the wave-guided type circuit for intra-body communication [5]. Aninput signal form a signal generator is ﬂowed in a palmthrough a pair of electrodes. On this occasion, a weak elec-tromagnetic wave is leaked and then propagated to anotherpair of electrodes. The propagated signal is detected andthen recorded by a receiver such as a digital oscilloscope.B. Dedicated Measuring DevicesIt is unrealistic and inconvenience to put electrodes on apalm every time measurement (authentication) is performed.Thus, we prepared dedicated measuring devices as shown inFig. 2.The left side one is made by making a plaster cast of apalm and the electrode is diverted form the metal part of agel-padded disposal electrode. The base of the right side oneis a mouse device and cupper plates are used as electrodes.The alignment of the electrodes in both devices is 5 cmlength and 2 cm width.(a) Plaster cast type (b) Mouse devicetypeFigure 2. Dedicated measuring devices for intra-palm propagation signals.Figure 3. A measurement scene using the dedicated measuring device.C. MeasurementBy using the dedicated measuring devices, we measuredintra-palm propagation signals from twenty-one subjects.The measurement was done twice on a day and repeated 30times (days). A measurement scene is shown in Fig. 3. Aninput signal from the signal generator was a pseudo whitenoise of 4 Vp¡p and 100 MHz bandwidth.The subjects sat on a chair and washed out stain onthier palm. After that, they put their palms on a dedicatedmeasuring device and kept themself at rest. In the meantime,intra-body propagation signals were measured by the digitaloscilloscope of which sampling frequency was 200 MHz andthen saved in a computer.III. VERIFICATION USING INTRA-PALM PROPAGATIONSIGNALSA. Feature ExtractionIn this study, the amplitude spectrum of an intra-palmpropagation signal is used as individual feature. However,User: A User: BUser: CUser: DUser: ZA Genuine Userthe Other UsersA Learning Model(a) 1vsALL SVMUser: A User: BUser: A User: CUser: A User: DUser: A User: ZA Genuine Userthe Other UserA Learning ModelA Learning ModelA Learning ModelA Learning Model(b) 1vs1 SVMFigure 4. Comparing of the balance of learning data between 1vsALLSVM and 1vs1 SVM.in order to suppress intra-individual variation, the spectrumis averaged and normalized. The averaging is achieved asfollows: measured intra-palm propagation data are equallydivided into several parts, an amplitude spectrum is obtainedfrom each part, and an averaged spectrum is obtained byensemble-averaging all amplitude spectra. The normalizingis achieved by equalizing the means of all amplitude spectra.B. Veriﬁcation Based on SVMIn Ref. [3], the veriﬁcation performance of intra-palmpropagation signals was evaluated based on the Euclideandistance between the spectral values at each frequencybin but it was not so high to conﬁrm that the intra-palmpropagation signal has the ability to be a new biometricmodality.In this paper, the Support Vector Machine (SVM) [6]is introduced into the veriﬁcation. The SVM is a strongclassiﬁer based on learning, of which advantage over otherclassiﬁers such as neural-networks is that the SVM has nolocal minimum problem. However, the SVM is basicallytwo-class classiﬁer; therefore, if the SVM is simply appliedin multi-class classiﬁcation (one versus all SVM: 1vsALLSVM), the unbalance of learning data is caused as (a) in Fig.4, where the solid-line box indicates the learning data foreach user and the dashed-line box presents a learning model.The unbalance of learning data brings the degradation ofveriﬁcation performance. Therefore, some ingenious schemeis required when the SVM is applied in veriﬁcation.As one of the schemes, we introduce the one versusone (1vs1) SVM [7], which constructs a learning modelcomparing a genuine user with the other (another) user. The1vs1 SVM resolves the unbalance problem shown as (b)in Fig. 4. On the other hand, the 1vs1 SVM needs manylearning models and a ﬁnal decision rule for considering theAMeasurementAll UsersB CZAveraging & NormalizingFeature ExtractionLearning by 1vs1 SVMAvsB AvsC AvsZBvsCBvsZYvsZAvsDBvsDLearning ModelsLearning MeasurementAn Applicant as “B”?Averaging & NormalizingFeature ExtractionVerification Evaluation by ModelsSpecify the Models for “B”BvsA BvsC BvsZBvsDDecision by Majority Rule+/- +/- +/- +/-Figure 5. Veriﬁcation procedure based on 1vs1 SVM.results from the models corresponding to a genuine user. Inthis study, we use the majority rule.Figure 5 shows the proposed veriﬁcation procedure basedon 1vs1 SVM for the intra-palm propagation signal. For allusers of a system, models are constructed in the learningstage. Each model is learned by teaching to output “+1“for the intra-palm propagation spectra of a genuine user and“-1“ for those of another user.In the veriﬁcation stage, ﬁrstly, an applicant of the systempresents genuine user‘s name and then his/her intra-palmpropagation signal is measured once. After smoothing, nor-malizing, and feature extracting, the intra-palm propagationspectrum is evaluated in all models corresponding to thegenuine user. Then if the number of the models that outputpositive value is larger than a threshold, that is, based on themajority rule, the spectrum is regarded as of the genuine userand the applicant is permitted to use the system.IV. VERIFICATION EXPERIMENTSA. ConditionsIn order to evaluate the veriﬁcation performance of theintra-palm propagation signal based on the 1vs1 SVM, wecarried out the veriﬁcation experiment using the intra-palmpropagation signals measured in Sect. II.SVMlight [8] was used as SVM tool kit. Forty data(spectra) from each subject were used for the learning ofmodels and the rest twenty data from each subject wereused for veriﬁcation.The spectral amplitudes at one hundred frequency bins(dimensions) were obtained by the FFT and then theywere equally divided into ten parts. In this experiment,ten amplitude spectral values in the optimal part for eachuser (subject) were used as individual feature. Not onlythe optimal part of the frequency band but also the op-timal parameters for the SVM were found by using thegrid searching, that is, round-robin formula. The parameterranges are summarized in Table I.Table IPARAMETER RANGES IN THE GRID SEARCHING.Cost Parameter0.001, 0.002, ¢ ¢ ¢ , 0.009,0.010, 0.015, ¢ ¢ ¢ , 0.095,0.10, 0.15, ¢ ¢ ¢ , 0.95,1.0, 2.0, ¢ ¢ ¢ , 9.0, 10.0Kernel FunctionPolynomial: d 1, 2, 3RBF: ±0.001, 0.002, ¢ ¢ ¢ , 0.009,0.010, 0.015, ¢ ¢ ¢ , 0.095,0.10, 0.15, ¢ ¢ ¢ , 0.95,1.0, 2.0, ¢ ¢ ¢ , 9.0, 10.0B. ResultsFigure 6 shows error rate curves: False Rejection Rate(FRR) and False Acceptance Rate (FAR). The horizontalaxis corresponds to a threshold value in the majority rule.From these results, it is conﬁrmed that the Equal Error Rate(EER) was approximately 25 % without relation to the shapeand the material of devices.Even using the SVM as a powerful veriﬁcation method,the veriﬁcation rate was not so high yet. Therefore, it isnecessary to reexamine the measuring method of intra-palmpropagation signals and the feature extraction method fromthem. For instance, a white noise was used as an input signalin order to obtain all spectral values efﬁciently. However,the variation of the spectral values of the white noise islarger and then it might increase intra-individual variationin the intra-palm propagation spectrum and degraded theveriﬁcation performance. In addition, using the white noisemade impossible to use the phase information of propagatedsignals.We are now examining the use of a speciﬁed signal thatconsists of several sinusoidal waves with different phases.This scheme suppresses the intra-individual variation andincreases the dimension of an individual feature. Therefore,the veriﬁcation performance may be improved.V. CONCLUSIONSWe have proposed the use of intra-palm propagationsignals as biometrics. However, the veriﬁcation performanceof the conventional method was low. Thus, we introducedthe SVM into the veriﬁcation stage and evaluated the veri-ﬁcation performance. As a result, we obtained the EER ofapproximately 25 % among twenty-one subjects.There are some problems to be studied further. Reexami-nation of methods for measuring of intra-palm propagationsignals and feature extracting from them is necessary inorder to achieve higher veriﬁcation rate. In addition, the(a) Plaster cast type(b) Mouse device typeFigure 6. Error rate curves.number of subjects must be increased for improving the reli-ability of the results obtained in the experiments. Moreover,it is conﬁrmed that the variation of the electrode position ona palm inﬂuences on veriﬁcation performance. Therefore,a new structure of the measuring device is required forstabilizing the electrode position.ACKNOWLEDGMENTA part of this work was supported by JSPS KAKENHIGrant Number 22560377.REFERENCES[1] A. Altinok and M. Turk, “Temporal Integration for ContinuousMultimodal Biometrics”, Proc. of 2003 Workshop on Multi-modal User Authentication, pp. 207-214, Dec. 2003.[2] I. Nakanishi, S. Baba, and C. Miyamoto, “On-Demand Biomet-ric Authentication of Computer Users Using Brain Waves”, inZavoral F. et al. (Eds.), NDT2010, Part I, CCIS 87, Springer,pp. 504-514, Jul. 2010.[3] T. Inada, I. Nakanishi, Y. Sodani, and Shigang Li, “BiometricAuthentication Using Intra-Palm Propagation Signals”, Proc.of International Technical Conference on Circuits/Systems,Computers and Communications (ITC-CSCC2012), Jul. 2012.[4] I. Nakanishi and Y. Sodani, “SVM-Based Biometric Authenti-cation Using Intra-Body Propagation Signals”, Proc. of 20107th IEEE International Conference on Advanced Video andSignal Based Surveillance (AVSS2010), pp. 561-566, Sep.2010.[5] K. Hachisuka, A. Nakata, T. Takeda, K. Siba, K. Sasaki, H.Hosaka and K. Itao, “Development of Devices for Commu-nication through Human Bodies (in Japanese)”, Micromecha-tronics, Bulletin of the Horological Institute of Japan, vol. 46,no. 2, pp. 53–64.[6] N. Cristianini and J. Shawe-Taylor, An Introduction to SupportVector Machines and other kernel-based learning methods,Cambridge University Press, 2000.[7] C. Hsu and C. Lin, “A Comparison of Methods for MulticlassSupport Vector Machines”, IEEE Trans. Neural Networks, vol.13, no.2, pp. 415-425, Mar. 2002.[8] T. Joachims, “SVMlight Support Vector Machine”,http://svmlight.joachims.org/, Aug. 2008.

Performance Evaluation of Intra-Palm Propagation Signals as Biometrics

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Performance Evaluation of Intra-Palm Propagation Signals as Biometrics

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