A study of the Graphical User Interfaces for Biometric Authentication System

Transcription

1 A study of the Graphical User Interfaces for Biometric Authentication System Hiroshi Dozono 1, Takayuki Inoue 1, Masanori Nakakun 2 i 1 Faculty of Science and Engineering, Saga University, 1-Honjyo Saga, JAPAN 2 Information Technology Center, Fukuoka University, , Nanakuma, Jonan-ku, Fukuoka JAPAN Abstract Recently, many mobile devices are equipped with touch panel, like smart phones, tablet devices, MP3 players and portable gaming consoles. These devices are also equipped with network devices for connecting internet, and they are accessible to personal information or pecuniary system such that net shopping or banking. For accessing such system, user authentication is required. In this paper, we propose an biometric authentication system using touch panel as input device. As the biometrics, behavior biometrics of moving images on the touch panel is used, and the user interface for acquiring the features of behavior effectively is studied. The effectiveness of the proposed method is examined by authentication experiments. Keywords: Biometrics, Authentication, Touch panel, Mobile device 1. Introduction Recently, many consumer devices, such as game consoles, tablet devices and mobile phones, are connected to the internet. These devices are used for web browsing, watching videos, and also for net shopping, trading and banking which require highly secure systems. As the authentication system, password authentication method, which is used on the conventional computers, is often used on these devices. However, many of these consumer devices are not equipped with keyboard, so on screen keyboard is used for typing password. During typing the screen keyboard, the typed characters are displayed on the display, thus typed password is easily peeped. For this problem, biometric authentication is considered to be effective. Biometric authentication is classified to biological biometrics and behavioral biometrics. Biological biometrics uses biological characteristics, such as fingerprint, iris pattern, face and vein pattern for authentication. Biological biometrics can be applied to the authentication system with high accuracy. However, special hardwares for detecting biological characteristics are required. On the other hand, behavior biometrics uses the features of the behaviors, such as hand written patters[1], key stroke timings[2], signatures[3] and walking patters, for authentication. Some of these features can be obtained from the sensors conventionally equipped to the devices. However, accuracy of behavior biometrics is less than that of biological biometrics. In this paper, the features obtained from the touch panel are used for authentication. Recently, many devices are equipped with touch panels, thus this method can be widely applied to those devices. As the authentication system using touch panel, the hand written signatures are often used. However, the devices, which are used in home, are also used by children who cannot write signature, and it is difficult to write uniform signature on slippery screen or capacitive type touch screen which are recently used as multi-tap panel for many devices. We proposed an authentication method using the hand written symbol for the touch panel of resistance film type[1]. As the features for authentication, the pen speed and pen pressure are used as multi-modal biometrics which can improve the accuracy of authentication[4]. However, this method can not be applied to recent capacitive touch panel because it can not detect pen pressure. For this problem, we proposed an authentication method using the movement pattern of some images on the touch panel. The combination of the movement of images as the password and the speed of movement as behavior biometrics. Using the combination of password and behavior biometrics, the authentication system becomes more secure. In this paper, we propose some improvements for this method. At first, the path for moving images are specified on the screen as curvilineal lines, because we reported that the pen speed tracing on the curvilineal line is more adequate as behavior biometrics. Secondly, the selection of the images are extended to increase the patterns of combination used for password. Finally, the novel user interface for authentication is proposed to improve the usability and security. For each system, the authentication experiments are conducted. For evaluating the relative accuracy among the system, the simple authentication algorithm based on the thresholds are used. 2. Touch Panel Devices Recently many mobile devices are equipped with touch panel. There are some types of touch panel devices. In

2 past days, the touch panel devices using resistance films as sensors were mainly used. For this type of touch devices, special pen is used for inputting information or operating devices. Using resistance film touch panel, exact coordinate on the panel can be obtained, and it is possible to obtain pen pressure on the panel. The pen pressure contains rich features as behavior biometrics, and can be used for improving the accuracy of biometric authentication. However, resistance film touch panel is not sensitive enough to realize comfortable user interface, and it is impossible to detect multi-point on the touch panel to realize multi touch operation which is common for recent mobile devices. Recently, most of the mobile devices use capacitive touch panel. Especially, the touch panel device which uses projected capacitance are mainly used because of the multi touch capability of this type of touch panel. Capacitive touch panel is operated by nude fingers, and can not be operated by solid pens which are used for resistance film touch panel. Because of this limitation, the accuracy of the coordinate detected by capacitive touch panel is worse than that of resistance film touch panel, and it is impossible to detect pen pressures. However, capacitive touch panel is sensitive enough for realizing confirmable user interfaces for operating mobile devices, and it can realize the multi-touch user interfaces. In this study, the tablet PC equipped with capacitive touch panel is used in experiments. Fig.1 shows the device. This Figure 1: Tablet PC(FMV LIFEBOOK TH40/D) with capacitive touch panel tablet PC is operated in Windows 7. We developed the authentication system directly on this computer using Microsoft Visual Studio to avoid inefficient cross development environment which is required for developing Android or ios software. 3. Authentication System with Moving Images on Touch Screen(System-0) In this section, the authentication method with moving images on touch screen which is reported in [5] is mentioned. Fig.2 shows the display image of the system. This system Figure 2: The authentication system with moving images(sytem-0) is called as System-0. In each box on the upper side, a image is set. The user is authenticated with moving the images to the box on the lower side in the pre-registered order for each user. The authentication is performed with matching the order of moved images to pre-registered order and with matching behavior characteristics during moving the images on touch screen to that of registration process. At each authentication, the images are arranged in random order in the upper boxes. The x-y coordinates of the moving images can be obtained from touch panel during moving the images. As the feature vectors for authentication, the sequence of velocity of the pen at each sampling time is used because the coordinate data of moving on the same traces did not be suitable for authentication and capacitive touch panel can not detect the absolute coordinates exactly. To examine the accuracy of behavior biometrics, the authentication experiment is conducted without changing the order of the images for all users. All users moves the images in same order, and the authentication is performed using velocity data. For evaluating the relative accuracy among the system, the simple authentication algorithm based on the thresholds are used. For each i-th-feature used for authentication, average m i and standard deviation δ i is calculated from the data registered for authentication. For each test data x, it is authenticated as the specified user if difference of each feature x i m i is smaller than αδ where α is the parameter for tuning authentication accuracy. In the following experiments, α is tuned at the value of cross points of FAR(False Acceptance Rate) and FRR(False Reject Rate) as shown in Fig.3.

3 Figure 3: Cross point FAR denotes the rate of accepting false users, FRR denotes the rate of rejecting the true user, and TAR denotes the rate of accepting true user. Table 1 shows the result. Figure 4: The authentication system using curvilineal path (System-1) Table 1: Result of authentication experiment of System-0 A B C D E F G H I J avg For some user, good authentication accuracy over 70% is marked, however it was not good for more than half users. Almost all user move the images linearly from upper box to lower box, and the paths are not identical, thus the accuracy is degraded. 4. Authentication System using Curvilineal Path for Moving Images(System-1) System-0 cannot mark good accuracy of authentication because of the fluctuations of the paths of moving images. For this problem, the path is specified as line in the display, and the user trace the line with finger for authentication. To improve the accuracy again, the curvilineal paths are specified because we reported that the pen velocity during tracing curvilineal path was more adequate as biometrics than that of linearly path[1]. Fig.4 shows the display image of this system. The authentication experiment is conducted using the behavior biometrics of pen velocity, without using the order of the images as password. Table 2 shows the result. The authentication accuracy is obviously better than Table 2: Result of authentication experiment of System-1 A B C D E F G H I J avg that of System-0, and all users mark over 70% accuracy. 5. Authentication System using Separate Image Selecting Window(System-2) System-1 shows good accuracy of authentication considering that it only uses behavior biometrics of pen velocity. However, the number of combination of the order of the images is only 4! = 24, and this is too small as the variation of password. To increase the combinations, the extension of the number of images is considered to be simple method, however too long sequences causes forgetting the password. For this problem, the separate window which can display more images is introduced. After selecting a image, the window flipped to the window for moving the image.

4 As the images, Landolt Cs, which are usually used for the visual acuity expression are used. The availability for the user and robustness to the peeping can be tuned with changing the size of circles and gaps. For increasing variation of images, the colored Landolt Cs are used. Fig.5 and FIg.5 show the display image of the system. Table 3: Result of authentication experiment of System-2 A B C D E F G H I J avg Figure 5: Image selection window of System-2 As for the usage of the direction of Landolt Cs and colors used as passwords, 7 users used identical color for all images, and 5 users used Landolt Cs sequentially rotated in identical direction. It may be difficult for the user to memorize the combinations of arbitrary directions of Landolt Cs and colora. From this result it is better for the user to use familiar images as password. 6. The Authentication System using Circular Path (System-3) For the problem found in System-2, the authentication system using familiar images and Circular Path is proposed. FIg.7 shows the window for selecting images. In this exper- Figure 6: Image moving window of System-2 The user selects the image in the selection window. Then the window is flipped to image moving window, and the user move the image to the registered box tracing the lines. The authentication experiment is conducted using the behavior biometrics of pen velocity, without using the order of the images as password. Table 3 shows the result. The authentication accuracy becomes worth than that of System-1. The reason is considered that the length and curvature of the path becomes smaller for System-2 than that of System-1. The longer and the adequate curvature are considered to be important to extract the feature of pen velocity as biometrics. Figure 7: Image selection window of System-3 iment, the images of animals are used as familiar images. Fig.8 shows the window for moving mages. The user select a image on selection window in FIg.7. Then, the window is flipped to the image moving window shown in

5 Figure 8: The authentication system using circular path(system-3) Figure 9: Image selection window with changing contrast level Fig.8, and the user move the image to the registered box tracing the registered direction of the circle. Compared with System-2, the variations of destination places and moving directions are increased. If the system evaluates the length of total path is too short, the system can suggest inverse direction to the user during registration. The authentication experiment is conducted using the behavior biometrics of pen velocity, without using the destination places of the images as password. Table 4 shows the result. The accuracy Table 4: Result of authentication experiment of System-3 A B C D E F G H I J avg of authentication of System-3 using pen velocity as biometrics is almost as same as that of System-1. Considering that System-3 can provide more variations of password, System- 3 is much better than System-1 as authentication System. 6.1 Control of the visibility As mentioned in the previous section, System2- uses Landolt Cs as images for controlling the visibility of the images. The familiar images may be easily found out when it is peeped by another person. To control visibility, the contrast of image the screen is modified depending on the security level. Fig.9 shows the window for selecting images with changing contrast level. In this printed proceedings, it becomes much difficult to identify the images, however it is not so much difficult to identity on computer screen. We made experiment of measuring the angle of view of 5 users with changing the 5 level of contrast. Table 5 shows the result. For level 1 of contrast, the contrast of the window Table 5: Angle of view for each contrast level User Level1 Level2 Level3 Level 4 Level 5 A B C D E is not changes. For all users, the view of angle becomes narrower with changing the contrast to higher level. The visibility of the another user who is peeping the screen can be controlled with changing the contrast to appropriate level. 7. Conclusion In this paper, we propose the user interface of the authentication system using the touch panel as input device. With moving the images on the touch panel, the proposed system uses the password which is represented by the destination places and behavior biometrics of pen velocity. Proposed system marks good accuracy considering the simpleness of authentication method with using only pen velocity as biometrics and simple authentication algorithm. Security of this system will be guaranteed with using the destination places of images as password.

6 As the future work, the authentication accuracy should be more strengthened. In this paper, simple algorithm using threshold is applied for the fair evaluation. The more smart algorithm should be examined as authentication algorithm. And, in this paper, multi-touch capability is not applied to authentication system. It may possible to develop more effective authentication method using multi-touch input. References [1] Hiroshi Dozono and Masanori Nakakuni, et.al: The Analysis of Pen Pressures of Handwritten Symbols on PDA Touch Panel using Self Organizing Maps, Proceedings of the International Conference on Security and Management 2005, pp (2005) [2] F. Monrose and A.D. Rubin: Keystroke Dynamics as a Biometric for Authentication, Future Generation Computer Systems, March(2000). [3] J. J. Brault and R. Plamondon: A Complexity Measure of Handwritten Curves: Modelling of Dynamic Signature Forgery, IEEE Trans. Systems, Man and Cybernetics, 23:pp (1993) [4] Hiroshi Dozono and Masanori Nakakuni et.al: An Integration Method of Multi-Modal Biometrics Using Supervised Pareto Learning Self Organizing Maps., Proc. of the Internal Joint Conference of Neural Network 2008,(2008) [5] Hiroshi Dozono, Takayuki Inoue and Masanori Nakakuni,et.al, Study of Biometric Authentication Method using Behavior Characteristics on Game Consoles, Proc. of SAM 2009, (2009)