CHAPTER I INTRODUCTION
CHAPTER 4 RESULT AND DISCUSSION
4.2 Reduced Sensors Signature Verification System
4.2.3 Decision Threshold
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II {a) Similarity Factor Evaluation for Genuine no 1 with 200 Forged Signatures"
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Figure 20: Similarity factor plots for (a) Genuine 1, (b) Genuine 2
The value of decision threshold for the reduced sensors experiment is equal to 75% as shown in Figure 20. The threshold is expected to be higher than in section 4.1.3 for the same reason mentioned in the last section, that is, the reduced sensors data, A, carry less information than the original A.
45
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CHAPTERS
CONCLUSION AND RECOMMENDATION
This chapter summarized the finding of this work and give reconunendation on further work in this area.
5.1 Conclusion
An approach to signature verification problem with data glove as input device to online signature verification system is presented. The technique is based on the singular value decomposition in finding a set of singular vectors sensing the maximum energy of the signature. This limited set of vectors is referred to as the principal subspace of data glove output matrix A and used to model the signature. The average distance between the different principal subspaces is used for signature classification. In the first experiment, the optimal value of r as a compromise between the reduced dimensionality and truncated energy of matrix A was found. Next, the decision threshold is sought and the value of 70% for the similarity factor was found to give sufficiently low FRR and FAR. In the second experiment, we attempted to test the performance of the signature verification system by reducing the number of sensors in the data glove by half. The selection of 7 most prominent sensors was done based on the calculation ofF- value for each sensor. Next the decision threshold is sought and the value of 1.8% EER was found to give sufficiently low FRR and FAR.
This work has demonstrated the effectiveness of data glove as an input device for the system. The system has the potential to offer a high level of security for special applications, including banking and electronic conunerce. Off course, to reduce the cost of the glove, the number of sensors on the data glove can be reduced without affecting its performance in signature verification. This will result in lower cost of the system and make it available for the average consumer.
5.2 Recommendation
In this work, we tested the performance of the signature verification system with the data glove sensors being reduced to half. The performance of the system can be further experimented by evaluating it at difference number of reduced sensors.
From this test, the optimum number of sensors required for signature verification system can be determined which will reduce the cost of the system without affecting its performance.
47
REFERENCES
[I] http://www.bioid.com/sdk!docs/ About_EER.htm
[2] Nidal S. Kamel, Senior Member, Shohel Sayeed, Member and Grant A. EJlis, Senior Member,"Glove-Based Approach to Online Signature Verification", IEEE transaction on pattern analysis and machine intelligence, val. 30, NO. 6, June2008
[3] http://en.wikipedia.org/wiki/, Oct 2008
[4] L. E. Spence, A. J. Insel , and S. H. Friedberg, Elementary Linear Algebra: A Matrix Approach, Prentice Hall, 1999
[5] S. Haykin, Adaptive Filter Theory, Prentice Hall, 4th edition, pp.823
[6] D. C. Lay, Linear Algebra and Its Applications, 3'd ed.,Pearson Education
[7] V.C. Klema and A.J. Laub "The Singular Value Decomposition: Its Computation and Some Applications," IEEE Trans Autom. Control, vol. AC-25, pg. 164-176
[8] Ed. F Deprettere., "SVD and Signal Processing: algorithms, applications and architectures", North-Holland Publishing Co., 1989.
[9] G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd ed., Johns Hopkins University Press, Baltimore, MD, 1996.
[10] Z. Drmac, "On Principal Angles between Subspaces of Euclidean Space,"SIAM J. Matrix Analysis and Applications, vol. 22, pp. 173-194, 2000 ..
[11] A. S. Tolba, " GloveSignature: A Virtual-Reality-Based System for Dynamic Signature Verification," Digital Signal Processing Academic Press, vol. 9, pp. 241- 266, 1999.
[12] Mingming, M. and Wijesoma, W. (2000). Automatic Online Signature Verification based on Multiple Models. In CIFEr.'Ol: Computational Engineering in Financial Engineering Conference (pp. 30-33), IEEE.
[13] SVD and Signal Processing: Algorithms, Applications and Architectures, F.
Deprettere, ed., North-Holland Publishing Co., 1989.
49
APPENDICES
APPENDIX A DATA GLOVE
Expensive high-end wired gloves can also provide haptic feedback, which is a simulation of the sense of touch. This allows a wired glove to also be used as an output device. Traditionally, wired gloves have only been available at a huge cost, with the finger bend sensors and the tracking device having to be bought separately.
Concerned about the high cost of the most complete commercial solutions, Pamplona et al. propose a new input device: an image-based data glove (IBDG). By attaching a camera to the hand of the user and a visual marker to each fmger tip, they use computer vision techniques to estimate the relative position of the finger tips.
Once they have information about the tips, they apply inverse kinematics techniques { GirardMaciejewski 1985} in order to estimate the position of each fmger joint and recreate the movements of the fingers of the user in a virtual world. Adding a motion tracker device, one can also map pitch, yaw, roll and XYZ-translations of the hand of the user, (almost) recreating all the gesture and posture performed by the hand of the user in a low cost device.
One of the first wired gloves available to home users was the Nintendo Power Glove. This was designed as a gaming glove for the Nintendo Entertainment System.
It had a crude tracker and finger bend sensors, plus buttons on the back. In 200 I, Essential Reality made a similar attempt at a cheap gaming glove, this time for the PC: the P5 Glove. However, this peripheral never really became popular among garners. Ironically, even specialized stores are now selling the older and less performant Power Glove for a higher price than the more sophisticated P5 Glove.
Wired gloves are often called "datagloves" or "cybergloves", but these two terms are trademarks, belonging to Sun Microsystems (which acquired the patent portfolio ofVPL Research Inc. in February 1998) and Immersion Corporation (which acquired Virtual Technologies, Inc. and its patent portfolio in September 2000) respectively.
An alternative to wired gloves is to use a camera and computer vision to track the 3D pose and trajectory of the hand, at the cost of tactile feedback.
51
5DT Data Glove
The 5DT Data Glove Ultra has been designed to satisfy the stringent requirements of modern Motion Capture and Animation Professionals. It offers comfort, ease of use, a small form factor and multiple application drivers. The high data quality, low cross-correlation and high data rate make it ideal for realistic real time animation.
Features:
• Advanced Sensor
This new range of data gloves from 5DT features a completely redesigned sensor technology. The new sensors make these gloves more comfortable and give more consistent data across a large range of hand sizes. Cross-correlation has been reduced significantly.
• Bluetooth Wireless Option
A wireless option is available, based on the latest Bluetooth technology for high bandwidth, wireless connectivity up to a range of 20m. The wireless kit can run for 8 hours off a single battery pack. The battery pack can be exchanged in seconds when necessary.
• 5 and 14 Sensor Gloves Available
The 5DT Data Glove Ultra range is available in a 5 sensor and 14 sensor configuration with a host of options such as right- and left-handed versions.
• Cross-Platform SDK
The glove SDK is available for Windows as well as Linux and UNIX. It is also possible to interface the glove without the SDK since it has an open-source communications protocol.
• Interface Options protocol
The Ultra range of data gloves now comes standard with a USB interface, eliminating the need for an external power supply. An open-source, open-platform serial interface is available for workstation or embedded applications.
• Wide Application Support
The 5DT Data Glove is now supported in most of the leading 3D modeling and animation packages.
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APPENDIXB MATLAB CODING
The MA TLAB software was the most important software which used in this work, for calculating SVD. The data from data glove is a matrix in
n
x 14 dimension, n is the sampling time which differs from signature by signature, for example one signature's data is shown in the Table 5 which has dimension as 61 x 14.Table 2: Data generated by data glove
Glove 1 Glove 1 Glove 1 Glove 1 Glove 1 Glove 1 Glove 1 Glove 1 Glove 1 Glove 1 lndex/Midt Middle Ne< Middle Far Middle/Rin Ring Near
Glove 1 Glove 1 Glove 1 Glove 1 Thumb N•" Thumb Far Thumb/lnd Index Near Index Far
566 1511 1845 1222 2124
566 567 568 568 568 567 568 569 570 570 57>
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570 568 565 56>
558 556 556 557 560 56>
563 564 566 567 568 568 569 569 570 57>
572 572 57' 574 574 575 575 576 576 575 576 575 575
1512 1513 1514 1513 1512 1512 1511 1511 1511 1510 1510 1510 1511 1513 1515 1520 1523 1526 1528 1531 1540 1543 1546 1548 1550 1551 1551 1551 1548 1542 1531 1520 1514 1510 1507 1507 1507 1508 1508 1509 1510 1510 1511 1512 1513 1514 1515 1518 1519 1520 1521 1522 1523 1523 1524 1525 1524 1524 1525 1524
1846 1847 1848 1849 1851 1851 1852 1854 1854 1855 1856 1857 1858 1858 1860 1869 1883 1890 1894 1898 1903 1906 1907 1908 1909 1911 1912 1910 1905 1888 1854 1834 1829 1827 1828 1831 1833 1835 1837 1839 1841 1843 1845 1846 1846 1847 1848 1849 1849 1849 1848 1847 1847 1846 1845 1844 1843 1842 1842 1842
1222 1222 1223 1223 1222 1222 1223 1222 1222 1221 1221 1222 1223 1224 1225 1233 1236 1239 1241 1244 1248 1249 1249 1250 1250 1250 1251 1252 1255 1255 1249 1239 1230 1224 1221 1220 1219 1220 1220 1220 1219 1218 1218 1218 1218 1217 1217 1219 1219 1218 1217 1216 1213 1213 1212 1211 1211 1211 1211 1212
2124 2123 2124 2123 2123 2122 2122 2122 2122 2122 2123 2123 2125 2126 2128 2135 2137 2140 2143 2145 2149 2151 2152 2153 2154 2154 2154 2154 2153 2151 2145 2141 2138 2136 2135 2134 2133 2133 2133 2133 2133 2133 2133 2133 2134 2135 2135 2136 2137 2138 2138 2138 2139 2140 2140 2140 2140 2140 2140 2140
2734 1465 1679 2736 1917
2736 2736 2736 2736 2736 2736 2736 2736 2737 2737 2737 2737 2737 2741 2743 2750 2751 2752 2752 2752 2753 2754 2755 2757 2757 2758 2758 2758 2757 2755 2755 2755 2757 2758 2754 2753 2752 2752 2752 2752 2752 2752 2752 2752 2752 2752 2752 2752 2752 2752 2752 2753 2753 2753 2753 2753 2753 2753 2753 2753
1464 1463 1461 1459 1457 1456 1455 1453 1451 1449 1448 1447 1446 1443 1443 1444 1445 1445 1446 1446 1447 1448 1449 1449 1449 1449 1449 1449 1449 1447 1446 1447 1447 1446 1444 1443 1442 1440 1439 1439 1437 1437 1435 1434 1433 1432 1431 1431 1431 1432 1433 1433 1434 1434 1435 1435 1435 1434 1434 1435
53
1678 1677 1676 1676 1675 1674 1673 1672 1672 1672 1672 1671 1672 1672 1674 1678 1680 1682 1684 1686 1689 1691 1693 1694 1696 1696 1697 1697 1696 1695 1692 1688 1684 1683 1681 1680 1679 1679 1678 1677 1677 1676 1676 1675 1676 1677 1678 1679 1681 1682 1683 1683 1685 1685 1685 1685 1686 1686 1686 1686
2736 2736 2736 2736 2737 2737 2738 2739 2740 2742 2744 2745 2745 2742 2740 2737 2736 2736 2736 2736 2736 2735 2731 2731 2729 2728 2729 2729 2731 2735 2736 2736 2737 2739 2744 2746 2746 2748 2749 2750 2750 2751 2751 2751 2752 2751 2749 2741 2737 2737 2736 2736 2732 2728 2728 2727 2726 2725 2726 2726
1916 1915 1914 1913 1911 1910 1910 1909 1907 1905 .1904 1903 190.1 1900 1899 1899 1899 1900 .1900 1901 1903 1905 1905 1906 1906 1907 1907 1908 1908 1909 1909 1909 1909 1909 1909 1908 1907 1906 1905 1905 1904 1903 1901 1901 1900 1899 1898 1898 1899 1899 1900 1900 1900 1901 1902 1902 1902 1902 1902 1903
Ring Far 2138 2138 2138 2139 2139 2139 2138 2138 2138 2138 2138 2138 2137 2137 2137 2138 2140 2141 2142 2144 2145 2147 2148 2150 2151 2152 2152 2152 2152 2152 2152 2151 2149 2147 2145 2:144 2143 2143 2143 2142 2142 2141 2140 2140 2140 2140 2140 2140 2141 2142 2142 2143 2144 2144 2145 2145 2146 2146 2146 2146 2147
Ring/Little 3141 3140 3138 3136 3135 3135 3135 3:135 3134 3132 3129 3128 3128 3126 3124 3122 3122 3124 3126 3127 3129 3130 3133 3133 3134 3134 3134 3135 3135 3135 3135 3135 3135 3133 3133 3:130 3128 3124 3120 3120 3120 3120 3119 3112 3108 3105 3103 3103 3103 3103 3106 3109 3112 3120 3120 3121 3128 3130 3131 3133 3133
Little Near Little Far
1698 1900
1698 1900
1698 1901
1698 1902
1697 1696 1696 1696 1696 1695 1694 1694 1694 1694 1694 1695 1696 1696 1697 1697 1698 1699 1699 1700 1700 1700 1700 1700 1700 1699 1698 1695 1692 1689 1688 1687 1687 1687 1687 1687 1688 1688 1688 1688 1688 1689 .1689 1689 1690 1691 1692 1694 1695 1697 1699 1701 1702 1704 1705 1705 1706
1903 1902 1901 1901 1902 1901 1900 1901 1903 1904 1905 1905 1909 1911 1914 1915 1918 1923 1925 1927 1927 1928 1929 1929 1929 1929 1927 1922 1912 1906 1903 1903 1903 1903 1903 1903 1903 1902 1902 1901 1901 1900 1899 1900 1902 1905 1908 1910 1910 1912 1913 1915 1917 1919 1921 1923 1.924
In SVD matrices, the right singular vector was the matrix that is used for signature verification. The right singular vector can be calculated by inserting the signature data as a matrix to MA TLAB command window, such as a=[the data], next for finding SVD, the following codes shall be written command window:
» [al,a2,a3]=svd(a);
>>a3 a3 =
-0.0756 0.0099 -0.0993 -0.0371 0.5495 0.0857 0.2169 -0.3239 -0.1243 0.0129 -0.3657 0.5576 -0.1794 0.1707 -0.2021 0.3110 -0.1795 -0.1879 -0.0258 0.3870 0.2618 0.2783 -0.5837 -0.1825 0.3372 0.0731 0.0707 -0.0222 -0.2470 0.7353 0.2132 0.3076 0.3188 -0.0577 -0.3132 0.1146 0.0859 0.1019 -0.0689 -0.1257 -0.0275 -0.0542 -0.1630 0.3088 0.1912 0.181! -0.5704 -0.1993 0.4768 -0.3991 -0.0596 0.0265 -0.0889 0.0453 -0.2037 0.0354 -0.2838 0,0781 -0.3465 -0.0056 -0.1633 amos -0.1939 -0-0297 0.3296 -0.4810 0.0146 -0.0048 -0.0513 0.6167 -0.3653 -0.1411 -0.3993 0.1514 -0.1205 -0.0635 -0.4501 -0.3889 -0.2221 0.3173 0,2776 0.1333 -0.0744 -0.2037 -0.1918 -0.0680 0.5351 -0.0645 -0.0636 0.4038 -0.1093 -0.2870 0.1874 -0.0160 0.2038 0.2701 0.5055 0.0359 -0.2235 0.0451 -0.1863 -0.1519 -0.1774 0.2795 -0.0345 0.0516 0.2678 -0.2764 -0.4434 0.0629 -0.0412 -0.6530 -0.3638 -0.3772 0.040 0.6847 0.1618 -0.0771 0.2904 0.2529 0.0202 -0.2151 0.0877 0.0250 0.0879 -0.1083 -0.2531 -0.1862 0.2078 -0.0496 -0.0731 0.4521 0.0215 0.2519 0.1533 0.4248 -0.0006 -0.0738 -0.5859 0.1715 -0.2848 -0.0814 -0.077 -0.0353 -0.0577 0.0598 0.0766 0.0254 -0.2179 0.3477 -0.5381 -0.3600 0.4821 0.2424 -0.4154 -0.1805 0.4055 -0.3955 -0.0113 -0.4730 -0.2225 0.1639 -0.2848 -0.2331 -0.1145 0.0784 -0.1383 0.0072 -0.2252 -0.0540 0.0062 -0.2520 0.3956 0.0147 0.2588 -0.4323 0.1261 -0.1333 0.2358 -0.5983 -0.1007 -0.1221 -0.2538 0.1239 -0.2169 -0.3054 0.0462 -0.3345 0.3138 0.2572 0.4469 0.3565 0.2420 0.2672 0.2063 -0.0429
After finding right singular vectors for the specific signatures that we want to measure their similarity factor, we shall save their right singular vectors in an M-file and then apply the cosine value between them, such as following M-file:
echo off clear all close all
a=[-0.1194 -0.5598 -0.6836 0.1954 0.2068 -0.3289 0.1258 -0.2831 0.3363 -0.3510 -0.7952 0.1088 -0.1778 0.0883 -0.3497 0.3652 -0.3732 0.3102 -0.6881 0.0949 0.1623 -0.5077 -0.0962 -0.0179 0.0101 0.3800 0. 72 54 0.2491 -0.5002 0.3604 0.2232 0.4085 0.4067 -0.4596 0.1628 -0.4084 -0.4454 0.1541 -0.0897 -0.3108 0.0663 0.7086 -0.3295 -0.3204 0.4430 -0.2414 -0.2558 -0.33070.5995];
b=[-0.1184 -0.2679 -0.7534 0.2602 0.3833 -0.3120 0.1860 -0.2784 0.4783 -0.2557 -0.7547 0.1907 -0.1285 0.0766 -0.3510 0.4366 -0.3740 0.3479 -0.6310 0.1634 0.0217