Chain Code Histogram based approach

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1 An attempt at visualizing the Fourth Dimension Take a point, stretch it into a line, curl it into a circle, twist it into a sphere, and punch through the sphere Albert Einstein Chain Code Histogram based approach 4.1 Preamble The design of computationally simple and accurate signature classification is highly challenging due to the intrinsic complexity of any off-line signature Extensive works have been reported in literature ranging from global approaches to local approaches Inspite of abundant works we have seen, most of the works are developed to their native language, such as Arabic, Persian, Bangla, etc In this context, we have made an attempt to develop a simple and accurate off-line signature verification approach based on chain code histogram suitable for our native language In addition, the proposed approach also works well even to English signature dataset We propose a 4 directional chain code histogram based approach with a goal to efficiently locate local features contributing for the signature recognition The approach preserves the directional information extracted from the signature contour and enhances its robustness by the application of Laplacian of Gaussian filter For extraction of local invariant features, a 12 x 12 grid is traversed along the preprocessed signature contour and 4 directional chain code is extracted into 4 different

2 matrices Each matrix bundles the local features within it and is characterized \ by a histogram of visual features m 4 directions Two additional matrices are calculated from these 4 normalised 4 directional matrices Thus, all the six matrices are passed through Laplacian of Gaussian filter to enhance the representative features The remainder of the chapter is organized as follows A background to the proposed approach is addresed m section 4 2 In section 4 3, the proposed approach is presented Experimental results are given m sections 4 4 and 4 5 Conclusions of the chapter are drawn m section Background As we all know, chain codes are one of the shape descriptors which are used to represent a boundary by a connected sequence of straight line segments of specified length and direction This representation is based on 4-connectivity or 8-connectivity of the segments Cham codes are a linear structure that results from quantization of the trajectory traced by the centers of adjacent boundary elements m an image array A chain code can be generated by following a boundary of an object m a clockwise direction and assigning a direction to the segments connecting every pair of pixels The major merits are (1) Cham codes are a compact representation of a binary object, (2) the chain codes are a translation invariant representation of a binary object, (3) the chain code is a complete representation of an object or curve, i,e we can compute any shape feature from the chain codes, (4) chain codes provide a lossless compression and preserving all topological and morphological information which bring out another benefit m terms of speed and effectiveness for the analysis of patterns On the other hand, the Laplacian is a 2D isotropic measure of the 2nd spatial derivative of an image and highlights the regions of rapid intensity change, henceforth used for edge detection Laplacian filter has the effect of emphasising edges m the image and magnifies the transition at edges The Laplacian filter applied 64

3 on Gaussian smoothed image is termed as Laplaeian of Gaussian (LOG), which removes both low and high frequencies from an image and operate as edge detectors, whilst managing to smooth out some of the noise too The LOG is an orientation independent filter that breaks down at comers, curves (where most of the signatures are visualised so) and at locations where image intensity function varies m a non-linear manner along an edge The two major benefits encountered from this approach are 1) As the grid bundles local features, the spatial contextual information enhances the discriminative matching, 2) Passing through LOG filter further enhances the features as it breaks down at corners, curves, and at locations where image intensity function varies m a non-lmear manner along an edge 4.3 Proposed approach \ The proposed chain code histogram based off-line signature verification approach involves three major phases In phase one, the signature sample image is preprocessed resulting in a contour In phase two, 4-directional chain code histogram is created using 12 x 12 grid passed through the signature contour, followed by the extraction of the enhanced features by applying the Laplaeian of Gaussian filter In phase three, the efficiency of the proposed approach is demonstrated using extensive experimentation on standard datasets with SVM and MLP classifiers The details are given m the following sub-sections Preprocessing Preprocessing is an essential phase to convert the acquired signature image to a form where easily intended features are extracted In our work, the preprocessing includes bmarization, noise elimination that might have intruded due to bmarization and contour extraction Contour is a set of edges linked m order to represent the region boundary Contours also occur when line fragments are linked together-for example, when line fragments are linked along a stroke in a drawing 65

4 or sample of handwriting As we all know, the signature is a set of strokes comprising of curves, lines and holes, constituting a complete shape Hence, contour of the signature helps m preserving the intrinsic shape of the signature with lesser number of pixels compared to the original image The mam idea of this approach is to extract the directional based information and the pixels on the contour are enough to fetch the desired features Figure 4 1 (a)imtial image (b)bmarized and noise removed image (c)image contour Chain code histogram based feature extraction The signature image contour (Figure 4 1(c)) is the input to the chain code histogram based feature extraction technique and the overall algorithm is as follows 1 Divide the input signature contour image into 12x12 grid blocks 2 The 4-directional chain-code histogram is generated by tracing the contour along each block as shown m Figure 4 2 Tracing m 4-directions results m four matrices of size 12x12 created for each of the directions Let us name these matrices as Hm, Vm, LmandRm created by horizontal, vertical, left and right directional tracing respectively 3 Apply the Laplacian of Gaussian filter on each of the directional 12x12 matrices obtained in step(2) with a = 0 5 and the mask of size 5x5 (4 1) 66

5 {a) i (b> * (c) ) i t *» t» * t *» * 1 a 4 J» t (d) Figure 4 2 (a)direction number for 4-directional chain codes (b)a sample 4- connected object (c) Boundary of (b) (d) Obtaining the 4-connected chain code for the object m (b) and (c) 4 To normalize the elements m the matrix, each element of each matrix obtained by the step(3)is divided by the maximum value of the four matrices jl Tfl(lx(Hm, Vrnj Lmi Rm) (4 2) 5 Two additional matrices Sm and Pm are calculated using the existing 4 directional chain code matrices by pairing the Hm with Vm and Lm with Rm as the proportions between perpendicular directions are relatively stable features and leads to better verification accuracies [64] max(hm,vm) m%n{hm,vm) 0 if max(hmi Vm) ^ 0 otherwise (4 3) p 1 m mm(lm,rm) TTKIX(I*m: fim } 0 if max(lmi Rrri) -f- 0 otherwise (4 4) Thus, by appending all the above six matrices (HmVmLmRmSmPm) the feature vector of size 12 x 12x6 is created Hence, a total of 864 feature vector is generated for the effective representation of the signature sample Accumulating the feature vectors of all the samples in the dataset, including genuine and skilled forge results in generation of the knowledge base The experiments are conducted with 8x8, 67

6 10x10 grid configurations and extended to 12x12, which produced the acceptable performance For LOG, the parameter a is set to 0 5 with a mask of size 5x5 4.4 Classification using MLP classifier Two set of experimentations are carried out with varying number of samples for training the MLP classifier In set-1 experiment, say SI, MLP classifier is trained with first 10 genuine and first 10 skilled forge of each signer During second set of experiment, say S2, first 15 genuine sample features along with first 15 skilled forge samples are considered for training In both the experimental set up, testing is carried out against the trained network with the remaining genuine sample features along with all skilled forge sample features Experimentation on CEDAR dataset with MLP classifier: To start with the experimental configuration SI, first 10 genuine and first 10 skilled forgery samples are considered to tram the MLP classifier The trained network is tested against the remaining 14 genuine samples along with 24 skilled forgery of the respective class In experimental set up S2, MLP is trained with first 15 genuine samples along with first 15 skilled forgery of the respective class The trained MLP network is tested against remaining 9 genuine samples along with all 24 skilled forge samples In the above two experimental set up, while training the classifier, skilled forgeries are used as the negative samples Table 4 1 gives the experimental results Experimentation on GPDS-100 dataset with MLP classifier GPDS-100, a sub-corpus of GPDS-300, consists of samples from first 100 classes of GPDS-300 In SI test configuration, MLP is trained with first 10 genuine and 68

7 Table 4 1 Experimental results on CEDAR dataset with MLP classifier SI 10G + 10SF 14G + 24SF S2 15G + 15SF 9G + 24SF Table 4 2 Experimental results on GPDS-100 dataset with SYM classifier SI 6G + 99RF 19G + 30SF S2 12G + 198RF 12G + 30SF first 10 skilled forgery samples features The trained network is tested against the remaining 14 genmne samples along with 30 skilled forgery of the respective class In experimental set up S2, MLP is trained with first 15 genuine samples along with first 15 skilled forgery of the respective class The trained MLP network is tested against remaining 9 genmne samples along with all 30 skilled forge samples The experimental results on GPDS-100 is tablulated m Table Experimentation on MUKOS dataset with MLP classifier In set SI configuration first 10 genmne samples along with first 10 skilled forge sample features are considered to tram the MLP classifier The remaining 10 genuine and all the 20 skilled forge samples of respective classes are tested against the trained network Similarly, m set S2, first 15 genuine samples along with first 15 skilled forge sample features are chosen for training Testing is carried out 69

8 Table 4 3 Experimental results on MUKOS dataset with SVM classifier SI 10G + 10SF 10G + 20SF S2 15G + 10SF 5G + 20SF on the remaining 5 genuine and all the 20 skilled forgery samples of the class The experimental results on MUKOS dataset with MLP classifier is tabulated m Table Classification using SVM classifier Here, the practical applications of signature verification are considered to design the experimental set up using SVM classifier The SVM classifier is trained with genuine signature features as positive samples (class 1) and random forgeries as negative samples (class 0) Two set of experimentation is carried out with varying number of training and testing sample features In the first experimental set up say SI, 25% of genuine signatures from each class along with one random forge sample from the remaining classes are randomly chosen for training the classifiers In the second experimental set up, say S2, we have randomly chosen 50% genuine samples from each class as positive samples with two random forge samples from the remaining classes as negative samples to tram the classifiers In both the experimental setup, testing is carried against the trained network with the remaining genuine samples and all the skilled forge samples of the signer from the respective dataset The random forge samples are the genuine signatures of other signers of the dataset We have followed leave out one policy while choosing the random forge sample for training To overcome the effect of randomness, experimentation is repeated 5 times and the 70

9 Table 4 4 Experimental results on CEDAR dataset with SYM classifier SI 6G + 54RF 19G + 24SF S2 12G + 108RF 12G + 24SF average accuracy is tabulated To demonstrate the performance of the proposed approach, experimentations are carried on standard datasets CEDAR, GPDS- 100 and MUKOS Experimentation on CEDAR dataset with SVM classifier We started with SI experimental configuration with 6 genuine and 54 random forgery samples chosen randomly to tram the SVM classifier To tram the classifier with the negative samples, (random forgery) one genuine sample from other 54 class is considered (6 positive samples + 54 negative samples) The trained network is tested against the remaining 18 genuine samples along with 24 skilled forgery of the respective class In experimental set up S2, SVM is trained with randomly chosen 12 genuine samples along with 108 random forgery Here, two genuine samples from each class m the respective dataset is chosen randomly The trained SVM network is tested against remaining 12 genuine samples along with all 24 skilled forge The above two experimental set up is repeated five times in order to overcome the effect of randomness and the average accuracy is noted m Table

10 Table 4 5 Experimental results on GPDS-100 dataset with SVM classifier SI 6G + 99 RF 19G + 30SF S2 12G + 198RF 12G + 30SF Experimentation on GPDS-100 dataset with SVM classifier GPDS-100, a sub-corpus of GPDS-300, consists of samples from first 100 classes of GPDS-300 In SI test configuration 6 genuine samples along with 99 random forgeries are randomly chosen to tram the SVM The 99 random forgeries are the samples taken from each class other than the one considered as positive sample to tram the SVM Testing was conducted against the remaining 18 genuine samples and all 30 skilled forge samples of the respective signer Experimentation set S2 is carried out by randomly choosing 12 genmne samples from each class along with 198 random forgeries Randomly selected two genuine samples from each class other than the training class considered as negative samples to tram the SVM Here, the trained network is tested against the remaining 12 genuine samples along with 30 skilled forge samples of respective class In order to overcome the effect of randomness, we repeated the above two experimentation for five times on all 100 classes and the average accuracies are noted and tabulated m Table Experimentation on MUKOS dataset with SVM classifier In set SI configuration 5 genuine samples along with 29 random forge are randomly chosen to tram the SVM classifier The remaining 15 genuine and all the 20 skilled forge samples of respective classes are tested against the trained network 72

11 Table 4 6 Experimental results on MUKOS dataset with SVM classifier SI 5G + 29RF 15G + 20SF S2 10G + 58RF 10G + 20SF In set S2, 10 genuine samples along with two random forgery samples are chosen randomly for training Testing is carried with the remaimng 10 genuine and all the 20 skilled forgery samples The best performances of both the experimentar tions are tabulated in Table 4 6 The metric values m the table are the average of five instances of the experimentation SI and S2 4.6 Conclusion In this chapter, we proposed to design a computationally simple and accurate off-line signature verification approach based on chain code histogram In the proposed approach, a 12 x 12 grid is passed through the signature contour to obtain 4 directional chain code histogram Thus, obtained features are enhanced through Laplacian of Gaussian filter, resulting m much effective and efficient representative features of the sample The performance of the proposed approach is exhibited using SVM and MLP classifiers Extensive experimentation on standard datasets reveals the accuracy and efficiency of the approach 73

NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: VOLUME 2, ISSUE 1 JAN-2015

NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: VOLUME 2, ISSUE 1 JAN-2015 Offline Handwritten Signature Verification using Neural Network Pallavi V. Hatkar Department of Electronics Engineering, TKIET Warana, India Prof.B.T.Salokhe Department of Electronics Engineering, TKIET