The Secure Grip project1 focuses on the development of a hand-grip pattern recognition system, as part of the smart gun. Its target customer is the police. To explore the authentication performance of this system, we collected data from a group of police officers, and made authentication simulations based on a likelihood-ratio classifier. This smart gun system has been proved to be useful in the authentication of the police officers. However, its authentication performance needs some further improvement, especially when data for training and testing were collected with some time in between. We present and analyze the simulation results of the authentication experiment. Based on the analyses, we propose some methods to improve the system¿s authentication performance

Bazen, A.M.

Ganzevoort, W.P.T.

Shang, X.

Veldhuis, R.N.J.

English

Veldhuis, Raymond N.J.

NARCIS 

Algorithm Design for Grip-Pattern Verification in Smart Gun

University of Twente Research Information

Algorithm Design for Grip-Pattern Verificationin Smart GunX. Shang, R.N.J. Veldhuis, A.M. Bazen and W.P.T. GanzevoortUniversity of Twente, EEMCS-SASP.O. Box 217, 7500 AE Enschede, the NetherlandsPhone: +31 (0)53 4892 842 Fax: +31 (0)53 4891 060E-mail: x.shang@el.utwente.nlAbstract— The Secure Grip project1 focuses on thedevelopment of a hand-grip pattern recognition sys-tem, as part of the smart gun. Its target customer isthe police. To explore the authentication performanceof this system, we collected data from a group of policeofficers, and made authentication simulations based ona likelihood-ratio classifier. This smart gun system hasbeen proved to be useful in the authentication of the po-lice officers. However, its authentication performanceneeds some further improvement, especially when datafor training and testing were collected with some timein between. We present and analyze the simulation re-sults of the authentication experiment. Based on theanalyses, we propose some methods to improve the sys-tem’s authentication performance.Keywords— grip pattern recognition, smart gun,likelihood-ratio, support vector machineI. I NTRODUCTIONIn grip pattern recognition the main research questionis, whether the pressure pattern exerted while holding anobject can be used to reliably authenticate a person. TheSecure Grip project focuses on the development of a pro-totype recognition system, as part of the smart gun, wherethe grip-pattern recognition ensures that it can only be firedby its rightful user. This application is intended for use bythe police, since carrying a gun in public brings consider-able risks. In the US vital statistics show that about 8% ofthe law-enforcement officers, who are killed in a shootingincident, are shot by their own weapon [1].The first prototype of this smart gun system (see Fig. 1)was described in [2], in terms of its design, implementationand evaluation. A collection of grip patterns was gatheredfrom a group of mostly untrained subjects with no expe-riences in shooting. The simulation results indicated that1This research is supported by the Technology Foundation STW, ap-plied science division of NWO and the technology programme of theMinistry of Economic Affairs.the grip pattern contains sufficient information to use forauthentication. To explore the authentication performanceof the smart gun system when used by its target customer,the police, we collected new data from a group of policeofficers. With the new data we made authentication simu-lations, by using the same algorithm as was used in [2].Fig. 1. Prototype of the smart gunThis paper presents and analyzes the authentication per-formance of the smart gun system, with the grip patternscollected from the police officers. Section 2 describes theauthentication algorithm. The procedure of data collectionis reviewed in Section 3. Subsequently, Section 4 presentsand analyzes the simulation results of the authenticationexperiment. Finally, the conclusions are given in Section5.II. A UTHENTICATION ALGORITHMThe authentication algorithm is based on a likelihood-ratio classifier for Gaussian probability densities. Thelikelihood-ratio classifier has been proved to be optimal,i terms of the expected overall error rates if the data hasa known probability density function [3], [4]. We assumethat both the overall data, and that of each individual sub-ject, are Gaussian distributed. The pixel values of eachgrip pattern image are aligned into a column vector, andare used as the features in the algorithm. In our system thefeature space has the dimension of 1936, since each grip674pattern was recorded as a 44 by 44 image.In practice, the exact probability density function of nei-ther the overall data, nor the data of each individual sub-ject is known, and therefore needs to be estimated fromthe training data. In total, four parameters need to be es-timated. They are the mean vector of the overall data, themean vector and the covariance matrix of each individualsubject’s data, and the transformation matrix. In our case,the number of training samples from each subject shouldbe much more than 1936. This is to prevent the estimatedcovariance matrices from being singular, and to prevent theclassifier suffering from overtraining. Otherwise, the algo-rithm would suffer from the so-called small-sample-sizeproblem. However, we can not make this large number ofmeasurements, since it would be rather impractical for theuser enrollment.To solve the small-sample-size problem, we assume thateach subject has the same within-class covariance matrix.Thus, this common covariance matrix can be estimatedmore accurately, by using data from all the subjects. Asa further step to relieve the effect of the small-sample-sizeproblem, we reduce the dimension of the feature space.Firstly, we apply a PCA (Principal Component Analysis)to project the overall data to a subspace with the biggestvariances. In this new space, the directions contributing tothe authentication, are not more than the number of sub-jects minus one. And, these directions have the smallestvariances of each individual subject’s data [3]. Accord-ingly, a further dimension reduction is achieved, by apply-ing a PCA to each individual subject’s data, and discardingall the directions, except only 40 ones (41 subjects in total)with the smallest variances.III. D ATA COLLECTIONWe collected grip patterns from a group of 41 police of-ficers, in three sequential sessions. The three sessions hadapproximately one month and four months in between. Allsubjects in the group participated in both the first and thesecond session, with 25 grip pattern images recorded fromeach. In the third session, however, data was collectedfrom 22 subjects from the same group; and each subjectcontributed 50 grip pattern images.In both the first and the second session, we asked eachsubject to pick up the gun, aim it at an assumed target, holdit still, say “ready” as a signal for us to record the image ofhis hand-grip pattern (see Fig. 2), and then release the gunafter we finished recording. This whole procedure was re-peated by the subject until enough data was collected fromhim. We observed afterwards, that most of the subjects’hand-grip pattern, varied greatly on average from one ses-sion to the other.Fig. 2. An example of the hand-grip pattern imageTo reduce this type of variation, in the third session, wesounded a beep each time that we finished recording oneimage. This was to rule out the inaccuracies in the data,caused by the possibility that the subject released the gun,before his hand-grip pattern was recorded. In addition, foreach subject, each time when he picked up the gun, westarted recording his grip patterns until he released the gun.This type of movie-like recording is for further study ofthe grip patterns’ characteristics in the future. Apart fromthese, the data was collected in exactly the same way as inthe previous two sessions.IV. EXPERIMENT, RESULTS AND DISCUSSIONIn this section, simulation results of the authenticationexperiment are presented. Subsequently, these results areanalyzed from both the view point of the data character-istics and that of the algorithm limitations. Given theseanalyses, some methods are proposed to improve the au-thentication performance of the system.A. Experiment set-up and resultsWe made two types of simulations in order to evalu-ate the authentication performance. One was the within-session simulation, where data for both training and test-ing came from the same session; the other was the across-session simulation, where data collected in two differentsessions was used for training and testing, respectively.T e authentication performance was assessed by the over-all Equal-Error Rate (EER) of all the subjects. This wascalculated by taking into account all the likelihood-ratios,of both the genuine subjects and the impostors. In thewithin-session case, we averaged the overall EERs ob-tained from 20 runs of simulation as the final result. Ineach single run, 75% of the data was randomly chosen fortraining, and thus the rest 25% for testing. The simula-tion results are presented in Table I and Table II. For easy675comparison, the simulation result by using data collectedfrom the untrained subjects, is also presented in Table I, asSession 0.Session EER(%)0 1.411 0.632 1.013 0.38TABLE IWITHIN -SESSION SIMULATION RESULTSTraining session Testing session EER(%)1 2 8.782 1 7.021 3 23.183 1 15.822 3 19.363 2 19.09TABLE IIACROSS-SESSION SIMULATION RESULTSOne can see that when data for training and testingcame both from the same session, the reference algorithmworked fairly well, and gave fewer authentication errors,compared to the case where data collected from the un-trained subjects was used; while the same algorithm showsrelatively worse results, when data collected in two differ-ent sessions was used for training and testing, respectively.Also, we found out that, both the false reject rate (FRR)and the false acceptance rate (FAR) became bigger in theacross-session simulation, in comparison with their coun-terparts in the within-session simulation. Besides, the FRRincreased more than the FAR. To give an example, we takethe first and the second session for training and testing, re-spectively, and present these four curves in Fig.3.A police gun must have a very low FRR, to make ithighly unlikely, that the rightful user could not able to fireit. The current official requirement in The Netherlands,for example, is that the FRR of a police gun must be lowerthan 0.01%. Under this condition, the FAR should be aslow as possible. One can see from Fig.3, that in the within-session simulation, the FAR is expected to be less than10%, when FRR equals 0.01%. However, the FAR is morethan 90% under the same condition, in the across-sessionsimulation.Since in practice, there will always be a time intervalbetween the user enrolment and the live authentication, theFig. 3. FAR and FRR curves obtained from the within-sessionand the across-session simulationacross-session simulation results make more sense. To im-prove the across-session authentication performance, weneed to first find out the reasons for the unsatisfactory sim-ulation results.B. Data characteristicsComparing the data recorded from different sessions,we discovered big variations in most of the individual sub-jects’ hand-grip pattern images. That is, for a certain sub-ject, his hand-grip pattern varied greatly on average fromone session to another.Two main factors may contribute to the across-sessionvariations of the same subject. One is the intrinsic fac-tor. That is, for some biological reason, after a certainamount of time, a person’s hand-grip pattern tends tochange within some certain range. The other one is theextrinsic factor. Since the data acquisition conditions inthese three sessions were slightly different, this may alsocause the variations of across-session data. Specifically,in the third session we used a beep, while not in the othertwo sessions. As mentioned in Section 2, this may affectthe recording time, and consequently the grip pattern im-age. Also, two different experimenters were recording databy pressing a button on the laptop, in the first and the sec-ond session, respectively. This may also result in differ-ent recording times of images, from even the same sub-ject. Because it is very likely, that these two experimentersneed different amounts of time, to respond to the subject’s“ready” signal, or to press the button.To find out statistically, how a certain subject’s datavaries from one session to another, we did an experimentas follows. We first randomly split the test set into two676Fig. 4. Hand-grip pattern images of the same subject recordedin two different sessionsequal-sized subsets, namely subset A and subset B; andthen used, for example, subset A for testing. Among thosefour parameters to estimate in the algorithm, we estimatedthree of them from the training set, yet the fourth one fromsubset B. We averaged the overall EERs obtained from 50runs of this type of simulation as the final result. This givesan insight on how much the across-session variation in acertain parameter, affects the authentication performance.For example, we may plug in the mean vector of overallsubjects, estimated from the test set B; while use the otherthree parameters, estimated from the training set.Table III gives the simulation results. TrSe and TeSerepresent the training session and testing session, respec-tively; LM andLC represent the mean vector and the co-variance matrix of each individual subject’s data, respec-tively; GM represents the mean vector of the overall data;and TM represents the transformation matrix. To makecomparison easier, the across-session simulation resultsare shown in the third row, represented byRF.TrSe 1 2 1 3 2 3TeSe 2 1 3 1 3 2RF 8.78 7.02 23.18 15.82 19.36 19.09LM 2.95 1.53 2.70 2.95 3.10 3.36LC 9.09 7.66 23.63 15.56 20.85 18.18GM 8.89 6.99 23.27 15.79 19.66 19.36TM 5.46 4.92 14.30 22.20 16.66 19.16TABLE IIISIMULATION RESULTS IN EER(%)WITH ONE PARAMETERESTIMATED FROM SUBSETBObviously, the across-session variation in the meanvalue of each individual subject’s data, affects the authen-tication performance the most. When this type of varia-tion is reduced, the authentication performance will be im-proved dramatically. Fig.5 gives further information aboutthe changes, of the FRR and the FAR, in this type of sim-ulation. Still, we take the first and the second session fortraining and testing, respectively.Specifically, for most of the subjects, the pressure oftheir hand-grip patterns is distributed differently in differ-ent sessions (see Fig.4 for an example). Besides, for somesubjects, shifts in position of their hands in different ses-sions have been observed.Fig. 5. FAR and FRR curves obtained from the across-sessionsimulation and theLM-plug-in simulationC. Algorithm limitationsThe reference likelihood-ratio classifier is probability-density-based, which thus requires estimating the densityfunction (Gaussian density function in our case) from thetraining data, before classification. In our case it suffersfrom the small-sample-size problem, since the availabletraining set is relatively small, in comparison with the di-mension of the original feature space. We therefore ap-plied PCA twice to reduce the dimension of the featurespace. However, this strategy may not suppress the small-sample-size problem well enough. Recently it has beenproposed, that the small-sample-size problem and conse-quently the danger of overtraining, result from the highcomplexity of the classifier. In addition, the dimensionof the feature space by itself, is not a good measure of aclassifier’s complexity [5]. Thus, even in the space witha lower dimension, the likelihood-ratio classifier may stillsuffer from overtraining, and thus not have good general-ization performance.Another limitation of the reference classifier is the as-sumption of Gaussian distributed data. The use of PCA,implicitly assumes that the data has Gaussian probabilitydistribution. Also, the likelihood-ratio classifier is optimalin terms of the expected overall error rates, only when theprobability density function of the data is known. Thus, if677this assumption is violated, the reference classifier is sub-optimal.D. Possible Strategies to Improve the PerformanceTo improve the authentication performance of the sys-tem, we may first consider reducing the across-sessionvariation in each individual subject’s mean value. Conse-quently, we believe, the variations in the other three param-eters will be further reduced as well. Some preprocessingof the grip pattern images may help to achieve that. Forexample, in a hand-grip pattern image, we may try equal-izing the local pressure values. This may help to reduce thedifference in pressure distribution between two images, ofthe same subject. Also, registration may help to align thosegrip patterns, which suffer from shifts in different sessions.In addition, a method useful for face recognition, ElasticBunch Graph Matching, may be worth trying. It provesespecially efficient in coping with variations in the faceposition, size, pose, and expression [6]. These variationsare, at least to some extent, similar to the across-sessionvariations of the same subject in our problem. Thus, thismethod may bring some improvement in the authenticationperformance of our system.Also, we may turn to some other type of classifier. Tak-ing into account the limitations of the reference classifier,the Support Vector Machine (SVM) seems a promisingchoice. The SVM builds an optimal classifier, and mean-while controls its complexity to gain the generalizationpower. Further more, the SVM does not rely on any as-sumption specifically about data’s characteristics [5]. Be-sides, in some preliminary simulation results with the grippattern data, the SVM has been proved to be more robustto overtraining, compared to the reference classifier.Certainly, these two solutions could be combined. Anyproper preprocessing methods would also be helpful to anew type of classifier. Besides, it is very likely, that noclassifier could solve the overtraining problem in questionby itself, even if it has good generalization performance.V. CONCLUSIONSThis smart gun system has been proved to be useful forauthentication, with hand-grip pattern data collected fromthe police officers. This certainly ensures its potential tobe used by the police in the future. However, its authenti-cation performance needs to be further improved to meetthe safety requirement of a real police gun.Simulation results of authentication experiment havebeen analyzed, in terms of both the data characteristics,and the algorithm limitations. We discovered a big varia-tion in the mean value of each individual subject’s data, indifferent sessions. This type of variation is the main reasonfor the unsatisfied performance of the current algorithm.Accordingly, we may apply some proper preprocessingmethods to reduce this variation. Also, improvements maybe expected, by using some other classifier, especially onewith better generalization performance. SVM, for exam-ple, may be a good choice.REFERENCES[1] The national Uniform Crime Reporting Program, “Law Enforce-ment Officers Killed and Assaulted”, tech. rep., Federal Bureau ofInvestigation, 2001.[2] Raymond Veldhuis, Asker Bazen and Joost Kauffman, “Biometricverification based on grip-pattern recognition”, presended atSPIE,2004.[3] Asker Bazen and Raymond N.J. Veldhuis, “Likelihood-Ratio-Based Biometric Verificaton”,IEEE Transactions on circuits andsystems for video technology, vol. 14, no. 1, January, 2004.[4] H.L. Van Trees,Detection, Estimation, and Modulation Theory,New York: Wiley, 1968.[5] V.N. Vapnik, The Nature of Satistical Learning Theory, Springer,1995.[6] Laurenz Wiskott, Jean-Marc Fellous, Norbert Krüger, andChristoph von der Malsburg, “Face recognition by Elastic BunchGraph Matching”,IEEE Transactions on pattern analysis and ma-chine intelligence, vol. 19, no. 7, July, 1997.678

Asker Bazen and Joost Kauffman, “Biometric veriﬁcation based on grip-pattern recognition”, presended at SPIE,

Face recognition by Elastic Bunch Graph Matching”,

Likelihood-RatioBased Biometric Veriﬁcaton”, IEEE Transactions on circuits and systems for video technology,

The national Uniform Crime Reporting Program, “Law Enforcement Ofﬁcers Killed and Assaulted”, tech. rep., Federal Bureau of Investigation,

The Nature of Satistical Learning Theory,

Trees, Detection, Estimation, and Modulation Theory,

Algorithm design for grip-pattern verification in smart gun

Algorithm design for grip-pattern verification in smart gun

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