=Paper=
{{Paper
|id=None
|storemode=property
|title=Static Signature Verification by Optical Flow Analysis
|pdfUrl=https://ceur-ws.org/Vol-768/Paper7.pdf
|volume=Vol-768
|dblpUrl=https://dblp.org/rec/conf/icdar/ImpedovoP11b
}}
==Static Signature Verification by Optical Flow Analysis==
https://ceur-ws.org/Vol-768/Paper7.pdf
Proceedings of the 1st International Workshop on Automated Forensic Handwriting Analysis (AFHA) 2011
Static Signature Verification by Optical Flow
Analysis
D. Impedovo, Member, IEEE , and G. Pirlo, Member, IEEE
local stability function can be obtained by using DTW to
Abstract—This paper presents a new approach for static match a genuine signature against other authentic specimens.
signature verification based on optical flow. In the first part of Each matching is used to identify the Direct Matching Points
the paper, optical flow is used for estimating local stability of (DMPs), that are unambiguously matched points of the
static signatures. In the second part, signature verification is
performed by the analysis of optical flow, using an alternating
genuine signature. Thus, a DMP can indicate the presence of a
decision tree. The experimental tests, carried out on signature of small stable region of the signature, since no significant
the GPDS database, demonstrate the validity of this approach and distortion has been locally detected. The local stability of a
highlight some direction for further research. point of a signature is determined as the average number of
time it is a DMP, when the signature is matched against other
Index Terms—Static Signature Verification, Local Stability, genuine signatures. Following this procedure low- and high-
Optical Flow.
stability regions are identified [7, 8, 9] in the selection of
reference signatures [10, 11] and verification strategies [12,
I. INTRODUCTION
13].
H ANDWRITTEN signatures occupy a very special place in
biometrics. Unlike other biometric traits, handwritten
signatures have long been established as the most widespread
A client-entropy measure has been also proposed to group
and characterize signatures in categories that can be related to
signature variability and complexity. The measure, that is
means of personal verification. Signatures are generally based on local density estimation by a HMM, can be used to
recognized as a legal means of verifying an individual's access whether a signature contains or not enough information
identity by administrative and financial institutions. Moreover, to be successfully processed by any verification system [14,
verification by signature analysis requires no invasive 15, 16].
measurements and people are familiar with the use of Other types of approaches estimate the stability of a set of
signatures in their daily life [1, 2, 3]. common features and the physical characteristics of signatures
Unfortunately, a handwritten signature is the result of a which they are most related to, in order to obtain global
complex generation process. The rapid writing movement information on signature repeatability which can be used to
underlying signing is determined by a motor program stored improve the verification systems [17, 18]. In general, these
into the brain of the signer and realized though his/her writing approaches have shown that there is a set of features that
system (arm, hand, etc.) and writing devices (paper, pen, etc.). remain stable over long time periods, while there are other
Therefore, a signature image strongly depends on the features which change significantly in time [19, 20]. Of course,
psychophysical state of the signer and the conditions under since intersession variability is one of the most important
which the signature apposition process occurs [4, 5]. causes of the deterioration of verification performances,
The net result is that signature variability is one of the most specific parameter-updating approaches have been considered
relevant issues that must be faced to develop accurate [18, 19, 20].
signature verification systems. In general, two types of Concerning static signatures, a multiple pattern-matching
variability can be distinguished in signing: short-term strategy has been recently proposed to determine - at local
variability and long-term variability. Short-term modifications level - the degree of stability of each region of a signature [21,
depend on the psychological condition of the writer and on the 22, 23]. In this paper the optical flow is used to estimate the
writing conditions. Long-term modifications depend on the local stability of the signature images. In addition, the optical
alteration of the physical writing system of the signer (arm and flow is also considered for signature verification, using an
hand, etc. ) as well as on the modification of the motor alternate decision tree classifier. The experimental results,
program in his/her brain [5, 6] carried out on signatures of the GPDS database, demonstrate
In literature, the approaches proposed for the analysis of the validity of the approach with respect to other techniques in
local stability are mainly devoted to dynamic signatures. A literature.
D. Impedovo and G. Pirlo are with the Dipartimento di Informatica, II. STATIC SIGNATURE ANALYSIS BY OPTICAL FLOW
Università degli Studi di Bari, via Orabona 4, 70125 Bari – Italy Two categories of signature verification systems can be
(corresponding author - e-mail: pirlo@di.uniba.it).
31
Proceedings of the 1st International Workshop on Automated Forensic Handwriting Analysis (AFHA) 2011
identified, depending on the data acquisition method [1]: static Figure 1 shows an example of Optical Flow: in (a) the
(off-line) systems and dynamic (on-line) systems. Static movement of a rectangle over two frames is shown; in (b) the
systems perform data acquisition after the writing process has optical flow vectors is reported.
been completed. In this case, the signature is represented as a
grey level image I(x,y), where I(x,y) denotes the grey level at III. ANALYSIS OF STABILITY OF STATIC SIGNATURES
the position (x,y) of the image. The results is that static In the next section, optical flow analysis is applied to the
systems involve the treatment of the spatio-luminance analysis of regional stability of static signatures. For this
representation of a signature image. Therefore, no dynamic purpose, after the preprocessing phase, in which each signature
information is available on the signing process when static is binarized and normalized to a fixed rectangular area, the
signatures are considered [1, 2]. Notwithstanding, static identification of the stable regions starts.
signature verification is very important for many application In particular, let be:
fields, like automatic bank-check processing, insurance form • Igi the set of N genuine signatures of a writer,
processing, document validation and so on. When static i=1,2,,…N;
signatures are considered, information on local stability is an • [uij(x,y), vij(x,y)]T the optical flow between Igi
important parameters for verification aims. In this paper local and Igj .
stability is analyzed by optical flow. Optical flow can be Now, if we consider the i-th signature Igi of a signer, for
defined as the distribution of apparent velocities of movement each pixel Igi(x,y) we can consider the sets of optical flow
of brightness patterns in an image I. As discussed in the vectors defined as:
excellent paper of O'Donovan [24], optical flow has been used
for a variety of computer vision applications like autonomous Ui ={uij(x,y) | j=1,2,…,N; j≠i }
navigation, object tracking, traffic analysis, image
segmentation and stabilization. Vi = {vij(x,y) | j=1,2,…,N; j≠i }.
In this paper we consider the approach of Horn and Shunck
for optical flow estimation [25]. In this case optical flow is The stability (S) of Igi(x,y) can be estimated as:
determined through the minimization of the energy functional
[25]:
S ( I ig ( x, y )) = σ u2 + σ v2
being σu and σv the standard deviation of Ui and Vi,
where
respectively.
• Ix, Iy and It are the derivatives of the image intensity
values along the x, y and time dimensions,
IV. SIGNATURE STABILITY BY OPTICAL FLOW
respectively;
• [uij(x,y), vij(x,y)]T is the optical flow vector; Optical flow provides useful information on local
dissimilarity among signature images. In this paper this
• α is the regularization parameter.
information is used for signature verification aims. In
In other words, the functional E consists of two terms: the
particular, signature verification is performed by an alternating
first term is the optical flow constraint equation and the second
decision tree (ADT). ADT, that was first introduced by Freund
is the smoothness constraint which is multiplied by the
and Mason [26], consists of decision nodes and prediction
regularization parameter α.
nodes. Decision nodes specifies a predicate condition,
prediction nodes contain a single number. Classification by an
ADT is performed by following all paths for which all decision
nodes are true and summing any prediction nodes that are
traversed. More precisely, in our approach, let be:
• Igi the set of N genuine signatures of a writer,
i=1,2,,…N;
• Ifp the set of M forgery signatures of a writer,
p=1,2,…,M.
(a) (b)
In the enrollment stage the ADT is trained by using the
Fig. 1. Example of Optical Flow. optical flow vectors concerning intra-class and inter-class
variability:
Horn and Schunk work out the previous minimization • [uij(x,y), vij(x,y)]T the optical flow between Igi and Igj ,
problem using a digital estimation of the Laplacian for the i,j=1,2,…,N, i≠j (intra-class variability);
optical flow gradients, to get a large system with two equations • [uik(x,y), vik(x,y)]T the optical flow between Igi and Igk ,
for each pixel that can be solved by the Jacobi method [25]. i=1,2,…,N, k=1,2,…,M (inter-class variability).
32
Proceedings of the 1st International Workshop on Automated Forensic Handwriting Analysis (AFHA) 2011
( a) (b)
Fig. 2. Example Analysis of Local Stability.
N
= 10 optical flows between genuine signatures and
V. EXPERIMENTAL RESULTS 2
The experimental results have been carried out using static N⋅M=20 optical flows between genuine signatures and
signatures of the GPDS database. The database contains 16200 forgeries are used for training. For testing, fourteen genuine
signatures from 300 individuals: 24 genuine signatures and 30 and fourteen forged signatures are considered. In the testing
forgeries for each individual [27]. The result here reported stage, the optical fields [uti(x,y), vti(x,y)]T between the test
concerns only twenty-five signers since other experiments are signature It and each genuine signature Igi, i=1,2,…,N, are
still in progress. For each signer the stability analysis is computed. Each one of the N optical flows is provided to the
performed, according to the approaches described in Section ADT that returns a verification results rti. The N results are
III. Figure 2 shows a genuine specimen (a) and the result of the combined according to the majority vote strategy, in order to
stability analysis obtained by optical flow (b). High stability define the final verification result for the test signature It.
regions are marked by continuous-line rectangles, low stability The results, in terms of Type I - False Rejection Rate (FRR)
regions are marked by dotted-line rectangles. In this case the and Type II - False Acceptance Rate (FAR) are reported in
stability analysis has been achieved by considering the three Table 1. On average we register a Type I error rate equal to
optical flows in Figure 3, obtained by computing the optical 23% and a Type II error rate equal to 20%. Figure 4 shows an
flows between the signature in Figure 2a and other three example of optical flow between two genuine specimens.
genuine specimens. Figure 5 shows the optical flow between a genuine specimen
Signature verification has been carried out by considering, and a forgery. The great amount of deformation is clearly
for each signer, N=5 genuine signatures (Igi, i=1,…,5) and visible when the optical flow is performed between a genuine
M=4 forgeries (Ifi, i=1,..,4) for training the ADT. Therefore, signature and a forgery.
( a) (b) ( c)
Fig. 3. Optical Flows between genuine signatures
33
Proceedings of the 1st International Workshop on Automated Forensic Handwriting Analysis (AFHA) 2011
TABLE I
Experimental Results
Author PERFORMANCE
n. FRR FAR
1 14% 36%
2 0% 0%
3 29% 0%
4 43% 57%
5 29% 14%
6 29% 43%
7 0% 0%
8 57% 14%
9 29% 57%
10 0% 0% Fig. 5. Optical Flow: genuine vs false
11 21% 29%
12 14% 0%
13 29% 50% VI. CONCLUSION
14 21% 14% In this paper optical flow is considered as a tool for static
15 0% 7% signature analysis. In the first part of the paper local stability
in static signatures is analyzed by optical flow analysis. In the
16 14% 14%
second part, optical flow vectors between test signature and
17 57% 29% genuine specimens are considered to verify the authenticity of
18 43% 36% a test signature, using an alternate decision tree. Some results
19 0% 0% carried out on static signatures extracted from the GPDS
20 21% 7% database demonstrate the new approach is worth consideration
21 14% 0% for further research. Of course, more experimental results are
necessary to verify the effectiveness of the proposed approach
22 14% 14%
and - in particular - to determine the capability of the Optical
23 57% 36% Flow in recognizing short-term and long-term variability as
24 36% 43% well as for evaluating the extent to which stability depends on
25 14% 7% the signature type and signer characteristics.
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34
Proceedings of the 1st International Workshop on Automated Forensic Handwriting Analysis (AFHA) 2011
(IWFHR-8), Ontario, Niagara-on-the-Lake, Canada, Aug. 2002, pp. [27] J.F. Vargas, M.A. Ferrer, C.M. Travieso, J.B. Alonso, “Off-line
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Reference Signatures for On-Line Signature Verification”, 8th laude" in Computer Engineering in 2005 and the Ph.D. degree in Computer
International Conference on Image Analysis and Processing (ICIAP-8), Engineering in 2009 from the Polytechnic of Bari (Italy). He is, currently,
Series: Lecture Notes in Computer Science, Vol. 974, Springer-Verlag with the Department of Computer Science of the University of Bari. His
Berlin, Heidelberg, C. Braccini, L. De Floriani and G. Vernazza (Eds.), research interests are in the field of pattern recognition and biometrics
San Remo, Italy, Sept. 1995, pp. 521-526. (speaker recognition ad automatic signature verification). He is co-author of
[11] V. Di Lecce, G. Dimauro, A. Guerriero, S. Impedovo, G. Pirlo, A. Salzo, more than 20 articles in these fields in both international journals and
L. Sarcinella, “Selection of Reference Signatures for Automatic conference proceedings.
Signature Verification”, Proc. 5th International Conference on He received "The Distinction" for the best young student presentation in
Document Analysis and Recognition (ICDAR-5), Bangalore, India, Sept. May 2009 at the International Conference on Computer Recognition Systems
20-22, 1999, pp. 597-600. (CORES - endorsed by IAPR). He is reviewer for the Elsevier Pattern
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“A Multi-Expert System for Dynamic Signature Verification”, 1st Image Processing and for many International Conferences including ICPR.
International Workshop, Multiple Classifier Systems (MCS 2000), Dr. Impedovo is IAPR and IEEE member.
Series: Lecture Notes in Computer Science, Springer-Verlag Berlin
Heidelberg, J. Kittler and F. Roli (Eds.), Vol. 1857, Cagliari, Italy, June G. Pirlo (IEEE member) received the Computer Science degree “cum laude”
2000, pp. 320-329. in 1986 at the Department of Computer Science of the University of Bari.
[13] L. Bovino, S. Impedovo, G. Pirlo, L. Sarcinella, “Multi-Expert Since then he has been carrying out research in the field of pattern recognition
Verification of Hand-Written Signatures”, 7th International Conference and image analysis. In 1988 he received a fellowship from IBM. Since 1991
on Document Analysis and Recognition (ICDAR-7), IEEE Computer he has been Assistant Professor at the Department of Computer Science of the
Society, Aug. 2003, Edinburgh, Scotland, pp. 932-936. University of Bari, where he is currently Associate Professor. His interests
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measure confronted with online signature verification systems' arithmetic, communication and multimedia technologies.
performance", Proceedings of the 2nd IEEE International Conference He has developed several scientific projects and published more than 150
on Biometrics: Theory, Applications and Systems (BTAS '08), papers in the field of document analysis and processing, handwriting
Washington, DC, USA, September 2008 recognition, automatic signature verification, parallel architectures for
[15] S. Garcia-Salicetti, N. Houmani, B. Dorizzi, "A client-entropy measure computing, communication and multimedia technologies for collaborative
for on-line signatures", Proceedings of the IEEE Biometrics Symposium work and distance learning. He served as reviewer for many international
(BSYM '08), pp. 83-88, Tampa, Fla, USA, September 2008. journals and conferences. Prof. Pirlo is member of the IEEE and of the IAPR -
[16] N. Houmani, S. Garcia-Salicetti, B. Dorizzi, “On assessing the International Association for Pattern Recognition TC11 (Technical
robustness of pen coordinates, pen pressure and pen inclination to time Committee on “Reading Systems”). He is also in the Governing Board of the
variability with personal entropy”, Proc. of IEEE 3rd International Italian Society for e-Learning (SIe-L).
Conference on Biometrics: Theory, Applications, and Systems, 2009
(BTAS '09), Washington, DC, Sept. 28-30, 2009, pp. 1 – 6.
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verification systems”, Pattern Recognition Letters, Vol. 27, N. 10, 15
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Workshop on Frontiers in Handwriting Recognition (IWFHR-9),
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features in on-line signature verification”, Pattern Recognition Letters,
Vol. 26, 2005, pp. 2483-2489.
[20] Y. Kato, D. Muramatsu, T. Matsumoto, “A Sequential Monte Carlo
Algorithm for Adaptation to Intersession Variability in On-line
Signature Verification”, Proc. 10th Int. Workshop on Frontiers in
Handwriting Recognition (IWFHR 10), La Baule, France, Oct. 2006.
[21] D. Impedovo, R. Modugno, G. Pirlo, E. Stasolla, “Handwritten
Signature Verification by Multiple Reference Sets”, Proc. of the 11th
International Conference on Frontiers in Handwriting Recognition
(ICFHR), 19-21 Aug. 2008.
[22] D. Impedovo, G. Pirlo, " On the Measurement of Local Stability of
Handwriting - An application to Static Signature Verification ", Proc. of
Biometric Measurements and Systems for Security and Medical
Applications (BIOMS 2010), September, 9, 2010, Taranto, Italy, IEEE
Computer Society Press, pp. 41-44.
[23] D. Impedovo, G. Pirlo, E. Stasolla, C.A. Trullo, "Learning Local
Correspondences for Static Signature Verification”, Proc. 11th Int. Conf.
of the Italian Association for Artificial Intelligence (AI*IA 2009),
December 9-12, 2009, Reggio Emilia, Italy.
[24] P. O'Donovan, “Optical Flow: Techniques and Applications”, The
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Algorithm”, Proceedings of the Sixteenth International Conference on
Machine Learning, Morgan Kaufmann, 199, pp. 124–133.
35