Extraction of global features for offline signature recognition - CiteSeerX

Extraction of global features for offline signature recognition Khalid Saeed and Marcin Adamski Faculty of Computer Science Bialystok Technical University Wiejska 45A, 15-351 Bialystok, Poland

http://aragorn.pb.bialystok.pl/~zspinfo/, Abstract: The paper presents experimental method for the extraction of global handwritten signature features with the aim of incorporating them in the offline signature recognition system. The algorithm uses view-based approach and searches for the extreme values with the threshold value being applied. Keywords: Signature Recognition, Global Features Extraction

1

Introduction

The handwritten signature is a very common way of authenticity. Despite its known weaknesses (relatively easy to copy, signatures of one person may vary significantly) and development of cryptographic and biometric techniques, it is still the most commonly used way of authentication when dealing with paper documents and forms. There are two main approaches for the signature recognition: offline and online. The offline methods analyze the static picture of the signature, whilst the online algorithms consider the dynamics of the writing process [2,3,7,8] as well. The algorithms that take into account only the static image are less resistant to forgeries but in many cases the static image of the signature is the only available form of information. This paper focuses on the analysis of the static features of a handwritten signature. The image of the signature is a special type of object when treated as the subject of the recognition process. One of the problems which is likely to arise is that the signatures of a particular person are not exactly the same. Of course, during the application of the recognition system we may require that the signatures should be made carefully but there are always some differences we must deal with. This requires that the identification system should be flexible and allow certain

variations within the set of the signatures put down by one person. The type of error we want to reduce at this moment is the rejection of the genuine signatures. On the other hand, in order to reduce misclassification and improve forgery resistance we must require that certain important features should be exactly recurrent and we must strictly demand their presence. The errors we are trying to minimize in this case are: acceptance of a fake signature and classifying one person’s signature as belonging to another one. Incorporating those two aspects – acceptance of the variance and the requirement for exactness of certain features in one system is a very difficult task and still there is no perfect solution. The techniques developed so far give good results but they are still affected by a relatively significant error. The alternative approach is to split those two aspects into separate tasks and then combine the results of both into one hybrid system. When human being attempts to handle signature recognition task it seems that the overall appearance or the general shape is the thing of highest importance. Only then, the particular features are examined in greater detail in order to make the correct judgment. Following the above considerations, this paper focuses on the general shape of the signatures in order to prepare data for the first recognition step. Information acquired at this stage should enable differentiation between the signatures given by different people and be general enough to reduce influence of the variations among different occurrences of the same signature. This stage should be supplemented by more precise local investigations to form the complete recognition system

2

Signature Acquisition

The input of the presented method is the signature object stored as a bitmap image (Fig. 1). The image can be obtained from documents by means of scanning devices. This approach can be applied to gather data from a variety of available sources in the form of signed forms because the signing on a paper form is still the most common authentication procedure.

Fig. 1. Examples of signature bitmaps

3

Selection of Characteristic Points

Reducing the dimensionality of data is a very important phase of every recognition process. In the case of static analysis we deal with the matrix representing the signature image. Every point belonging to the signature curve is the fundamental unit of information. In order to reduce the size of data while preserving the general features the view-based method [1,5] is applied first. The algorithm chooses only those points with minimal and maximal value of y coordinate. The process is illustrated in Fig 2.

Fig. 2. A signature and its outline To proceed further with the reduction of data we select N equally spaced points form the resulted outline where the value of N depends on the resolution of an image and requirement for generalization. During experiments N = 5 was chosen as it sufficiently decreased the dimensionality and preserved the general shape of the outline. As a result the two vectors for each of the signatures are obtained (Eq. 1). The normalization (Eq. 2) is applied to get values ranging in < 0,1 >

Y = y1 , y 2 ,..., y n −1 , y n

yi'=

yi − ymin ymax − ymin

(1)

(2)

4

Calculation of Extreme Values

The extraction of the extreme values from coordinates of vector Y reduces the influence of the variance among signatures of a particular person. Equations 3 and 4 represent the conditions which were tested to find the maximum and the minimum values accordingly. In addition, the threshold value is applied in order to eliminate disturbances created by roughness of the ink trace and minor artifacts which mostly are conducive to inaccuracy of the signing individual. If the difference between most recently acquired extreme and the currently examined one does not exceed the threshold (Eq. 5) value it is discarded from the final feature vector (Eq. 6).

5

yi < yi +1 ,

y i + k > y i + k +1 ,

yi+1 = yi+ 2 = ... = yi +k

(3)

yi > yi +1 ,

yi +k < yi +k +1 ,

y i +1 = y i + 2 = ... = y i + k

(4)

y ej − y e > T

(5)

Y e = y1e , y2e ,..., yme −1 , y me

(6)

Results

The following figures (Figs 3 and 4) illustrate the results achieved from the signatures taken from four individuals, each repeated three times. The documents where scanned in 150 dpi resolution and written to bitmap files.

1 0.9 0.8 0.7 0.6

yi

0.5 0.4 0.3 0.2 0.1 0

0

5

10

15

20

25

30

35

40

45

50

i Fig. 3. The upper views of selected signature.

1 0.9 0.8 0.7 0.6

yie

0.5 0.4 0.3 0.2 0.1 0

0

5

10

15

i Fig. 4. Extreme values of the upper views of selected signature Fig. 3 represents the upper views of one of the signatures calculated for N=50. Fig. 4 shows the results achieved after finding extreme values with

the threshold value T = 0.05 for normalized data. As can be seen, the lines representing each version of the signature have similar characteristics, though, in the first graph the variance is significantly higher. Experiments showed that individuals during signing process often stretch and shrink local parts of the signature. Additionally, there is an increasing variance towards the end of the signature denoting decreasing accuracy of writing. Fig. 4 shows that by taking into account extreme values we can decrease these negative features while maintaining the general shape which is stable across different occurrences of the same signature. Figs 5 and 6 present the results obtained for 4 signatures, each written twice. In Fig. 6 the number of extremes is constrained to 10 first values. 1 0.9 0.8 0.7 0.6

yi

0.5 0.4 0.3 0.2 0.1 0

0

5

10

15

20

25

30

35

40

i Fig. 5. The upper views of the signatures

45

50

1 0.9 0.8 0.7 0.6

yie

0.5 0.4 0.3 0.2 0.1 0

1

2

3

4

5

6

7

8

9

10

i Fig. 6. Extreme values of the upper views

Table 1 contains corresponding values illustrated in Fig. 6. Each of its columns (1-10) represents values of selected extremes. Table 1. Extreme values of the upper views 1

2

3

4

5

6

7

8

9

10

Sig. 1

0.37 0.32

0 0.04

1 0.93

0.39 0.35

0.97 1

0.44 0.37

0.94 0.94

0.16 0

0.53 0.39

0.16 0.04

Sig. 2

0.1 0.1

1 0.68

0.36 0.44

0.87 0.69

0 0

0.39 0.32

0.28 0.19

0.36 0.31

0.2 0.16

0.36 0.35

Sig. 3

0.42 0.42

0.72 0.75

0.18 0.07

0.34 0.33

0 0

0.44 0.34

0.08 0.1

0.37 0.38

0.07 0.02

0.35 0.15

Sig. 4

0.52 0.68

0.77 0.83

0.7 0.76

0.91 1

0.31 0.46

0.9 0.95

0 0

1 0.91

0.13 0.25

0.49 0.32

The graphs of Figs 5 and 6 show that after applying the descirbed algorithm to the original data, the separation of characteristics for each of the signatures is improved and the variance between different versions of the same signature is minimized. It is a very important result when we consider the problem of recognition because this kind of preprocessed information would be much easier to deal with. The presented method does not always give results that can be clearly separated on the graph representation. With certain types of data ( especially for down view) where the difference between consecutive extremes is small and near the threshold value, some of the important extreme values may not be included. This affects the characteristics after shifting (Fig. 7). This effect should not be much of a problem during recognition phrase because there are varieties of algorithms taking into account such local absence of information. These methods include Dynamic Time Warping [6,7] and Hidden Markov Models [8,9].

yie

i Fig. 7. Signature extremes after shifting

6

Conclusions and Future Work

The results of the presented method seem to be promising and hence encouraging to further work to incorporate them in the complete offline signature recognition system. The separation of the signature classes is clearly improved after application of the developed algorithm. The graphs representing signatures belogning to a particular individual fit closely together allowing for easier recognition. It is also obvious that such a system should be supplemented by additional data from local features like placement and size of internal loops, number of crossings, endings and so on. The future research will be focused on incorporating both global and local features in one hybrid solution.

7

References

[1]

K. Saeed, M. Tab dzki, M. Adamski, „A New Approach for ObjectFeature Extract and Recognition”, 9th International Conference on Advanced Computer Systems – ACS’02, pp. 389-397, Miedzyzdroje, 23-25 October 2002.

[2]

L. Lee, T. Berger, and E. Aviczer, “Reliable on-line Human Signature Verifiaction Systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 643-647, June 1996.

[3]

K. Saeed, "Efficient Method for On-Line Signature Verification" Proceedings of the International Conference on Computer Vision and Graphics - ICCVG' 02, vol. 2, pp. 635-640, Zakopane, 25-29 September 2002

[4]

C. Parisse, “Global Word Shape Processing in Off-Line Recognition of Handwritting”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 5, pp. 460-464, April 1996

[5]

K. Saeed, M. Tab dzki, “A New Hybrid System for Recognition of Handwritten-Script“, International Scientific Journal of Computing, Institute of Computer Information Technologies, vol. 3, issue 1, pp. 50-57, Ternopil, Ukraine 2004

[6]

P.A. Hall, G.R. Dowling, “Approximate string matching”, ACM Computing Surveys, vol. 12, no. 4, pp. 381-402, December 1980.

[7]

R. Martens; L. Claesen, “On-line signature verification by dynamic timewarping”, Proceedings of the 13th International Conference on Pattern Recognition, vol. 3, pp. 38-42, Vienna , Austria, August 1996

[8]

G. Rigoll, A. Kosmala, “A systematic comparison between on-line and offline methods for signature verification with hidden Markov models”, Proceedings of the 14th International Conference on Pattern Recognition, vol. 2, pp. 1755-1757, Brisbane, Australia, August 1998

[9]

M. Mohammed, P. Gader, “Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no.5, pp. 548-554 , May 1996