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Volume 48 – No.2, June 2012 6 [3] World Bank e-government.. http:go.worldbank.orgM1JHE0Z280 [Last Accessed on 15th march, 2012] [4] M. Al-Sebie and Z. Irani: , 2005.,Technical and organisational challenges facing transactional e- government systems: an empirical study, Electronic Government, an International Journal 2005 - Vol. 2, No.3 pp. 247 - 276. [5] A. Rabaiah and E. Vandijck. 2009. A Strategic Framework of e-Government: Generic and Best Practice, Electronic Journal of e-Government Volume 7 Issue 3, pp. 241 – 258, [6] A. Avny. 2007. SWOT analysis of e-Government Annals of University of Bucharest, Economic and Administrative Series, Nr. 1, pp. 43-54. [7] H. Weihrich. 1982. The TOWS matix- A tool for situational analysis. Long Range Planning. Vol 15, 2. pp. 54-66. [8] T.L. Saaty. 2008. Decision making with the analytic hierarchy process. International Journal of Service Sciences. Vol 1, No 1. [9] H.H. Cheng and W.C. Huang. 2006. 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Available from: www.ibimapublishing.comjournalsJEGSBP2011...82 0912.pdf [Last Accessed: 20th February 2012] [25] International Monitory Fund IMF, http:www.imf.orgexternalpubsftsurveyso2011int09 2111b.htm [Last Accesses 10th March, 2012] [26] Yesser Program Saudi Arabia. www.yesser.gov.sa [Last Access: 20 th February 2012] [27] Cisco Systems Inc, “Saudi Arabia University Deploys Advanced IT to Support New Education Model”. Available from: http:www.cisco.comenUSprodcollateralswitchesps 5718ps70832023_PMU.pdf [Last Accessed: 22th February 2012] [28] Council of Foreign Relations., 2011. Saudi Arabia in the New Middle East, [Last Accessed: 22th February 2012] [29] F. Al-Sobhi and V. Weerakkody., 2010. The role of intermediaries in facilitating e-government diffusion in Saudi Arabia, in proc. of European and Mediterranean Conference on Information Systems. [30] Regional Profile 2009. Information Society Portal for the ESCWA Region. http:isper.escwa.un.orgDefault.aspx?TabId=65item= 23 [Last Accessed 10 th March, 2012] [31] M. Alanezi., A.K.Mahmood and Shuib Basari. 2011. Review of e-Government Policy and Outcomes in the Kingdom of Saudi Arabia. International Journal of Computer Science and Information Security., vol 9, No 8. [32] J.X, Dempsey., P, Anderson. and A. Schwartz. 2003. E- government and Privacy [online]. Available: http:www.internetpolicy.netprivacy20030523cdt.pdf [Accessed: 25th February 2012] Volume 48 – No.2, June 2012 7 [33] P.K. Sarkar and M. Singh., 2008. Narrowing the digital divide: the Australian situation. International Journal on Computer Science and Information Systems. Vol. 3, No. 2, pp. 27-35. Saleh Alshomrani is a faculty member in the Information Systems Department at King Abdulaziz University, Saudi Arabia. He is also serving as the Vice-Dean of Faculty of Computing and Information Technology, and the Head of Computer Science Department – North Jeddah branch at King Abdulaziz University. He earned his Bachelor degree in Computer Science BSc from King Abdulaziz University, Saudi Arabia in 1997. He received his Master degree in Computer Science from Ohio University, USA in 2001. He did his Ph.D. in Computer Science from Kent State University, Ohio, USA in 2008 in the field of Internet and Web-based Distributed Systems. Currently he is actively working in the area of Web and Internet Systems. Shahzad Qamar is working as a Lecturer in Faculty of Computing and IT at King Abdulaziz University Saudi Arabia. Shahzad Qamar has done his MS in Wireless and Mobile Networks from Bournemouth University UK. Shahzad Qamar’s research focus is on the new emerging technologies like e-government, M-government, Green and Cloud Computing, QoS in WiMAX and WiFi Networks. Appendix A: Pair wise comparisons matrices for SWOT factors Strengths S1 S2 S3 S4 Local Weights S1: Political wiliness and Public Policy 1 7 7 7 0.68 S2: Citizens focused policy 17 1 2 2 0.14 S3: Good ICT Infrastructure in Saudi Arabia 17 12 1 2 0.10 S4: E-Government portal and sub-portals 17 12 12 1 0.007 availability Total 1.43 9 10.50 12 1 λ max = 4.208 CI = 0.0693 CR = 0.0770 Weaknesses W1 W2 W3 Local Weights W1: Lack of IT skills 1 17 13 0.08 W2: Digital divide 7 1 5 0.72 W3: Common culture on e-transactions 3 15 1 0.19 Total 11 1.34 6.33 1 λ max = 3.11 CI = 0.06 CR = 0.09 Opportunities O1 O2 O3 O4 O5 Local weights O1: Strong Economy of Saudi Arabia 1 15 3 5 1 0.18 O2: Potential in growth in ICT Infrastructure 5 1 7 7 1 0.41 O3: Legal Framework 13 17 1 2 17 0.06 O4: Participation of academics to support ICT 15 17 12 1 17 0.04 O5: Better opportunities of employment for 1 1 7 7 1 0.31 IT professionals Total 7.53 2.49 18.5 22 3.29 1 λ max = 5.36 CI = 0.09 CR = 0.08 Threats T1 T2 T3 T4 Local Weights T1: De-centralized Internet Governance 1 15 3 13 0.12 T2: Individual attitude and social culture 5 1 9 5 0.63 T3: Privacy and Security of personal information 13 19 1 13 0.06 T4: Use of mobile technology 3 15 3 1 0.20 Total 9.33 1.51 16 6.67 1 λ max = 4.26 CI = 0.0889 CR = 0.09 Volume 48 – No.2, June 2012 8 Sign Language Recognition in Robot Teleoperation using Centroid Distance Fourier Descriptors Rayi Yanu Tara 1 , Paulus Insap Santosa 2 , Teguh Bharata Adji 3 EE IT Department, Gadjah Mada University Yogyakarta, Indonesia ABSTRACT Commanding in robot teleportation system can be done in several ways, including the use of sign language. In this paper, the use of centroid distance Fourier descriptors as hand shape descriptor in sign language recognition from visually captured hand gesture is considered. The sign language adopts the American Sign Language finger spelling. Only static gestures in the sign language are used. To obtain hand images, depth imager is used in this research. Hand image is extracted from depth image by applying threshold operation. Centroid distance signature is constructed from the segmented hand contours as a shape signature. Fourier transformation of the centroid distance signature results in fourier descriptors of the hand shape. The fourier descriptors of hand gesture are then compared with the gesture dictionary to perform gesture recognition. The performance of the gesture recognition using different distance metrics as classifiers is investigated. The test results show that the use of 15 Fourier descriptors and Manhattan distance-based classifier achieves the best recognition rates of 95 with small computation latency about 6.0573 ms. Recognition error is occurred due to the similarity of Fourier descriptors from some gesture. Keywords Hand Gesture, Sign Language, Fingerspelling, CeFD, Fourier Descriptor.

1. INTRODUCTION

Hand gesture recognition provides a natural way to communicate with machines e.g. robot. The use of robot, especially in teleoperation, can reduce the risk factor of task failures and human harms during several activities such as hazardous material handling [1]. Sign language is often used as input method in teleoperation system. Several works that employ visually captured hand gesture in robot teleoperation system has been researched since last decade. Color camera was used to acquire hand image, and the acquired image was processed with Artificial Neural Network ANN [2], Fuzzy C-means clustering FCM [3], or Support Vector Machine SVM [4] to classify the meanings of each hand gesture. In a sign language, hand shape can give information of hand gestures. Recognizing gestures through hand shape is a challenging process. Some sign language has the similar hand shape, and similar hand shape can be interpreted as different sign because of different viewpoint. Due to its complexity, the research of hand gesture recognition based on hand shape is continuously performed. Recognition of hand posture using two different shape descriptors had been conducted in [5]. Fourier descriptors and hu moments were compared in this research. This experiment used 64 fourier coefficients. Two databases of gestures were used in this experiment i.e. Triecsch database [6] and self-made database. The result of this experiment showed that fourier descriptors give very good recognition rate rather than hu moments. Performance comparison of fourier descriptors and geometric moment invariants was presented [7]. The comparison was used ASL database to analyze discrimination and feature invariance of hand images. The results showed that both descriptors are unable to differentiate some classes in ASL. Another shape descriptor comparison in hand posture recognition from video was also presented in [8]. The research compares: 1 Hu moments, 2 Zenike moments, 3 Fourier descriptors common set, and 4 Fourier descriptors complete set. The recognition also evaluated the use of several classifiers to measure similarity of hand posture with the stored hand posture in the database. Bayesian classifier, support vector machine, k-nearest neighbor, and Euclidean distance were evaluated. Overall result of the presented research showed that the common set fourier descriptors has the highest recognition rate when combined with k-nearest neighbor, reaching 100 in classifiying learning set, and 88 in test set. An application of real time hand gesture recognition system using fourier descriptors was presented in [9]. The presented system uses 56 fourier descriptors from 64 fourier coefficients, and linear combination of Hidden Markov Models HMM and Recurrent Neural Network RNN. With real time processing rates of 22 frames per second and 91.9 correct classification; the presented system achieved good performance. Another approach of hand gesture recognition using fourier descriptors and hidden markov models was also introduced [10]. The system was able to recognize 20 different gestures with average recognition rate of above 90. All of the research in [5], [7], [8], [9], and [10] were used the same shape signature i.e. complex coordinate in fourier descriptors calculation and used hand image from color camera. An evaluation of the use of different shape signature in fourier descriptors calculation for shape retrieval was presented [11]. The research compared four shape signatures: 1 complex coordinate, 2 centroid distance, 3 curvature signature, and 4 cumulative angular function. Euclidean distance was used as similarity measurement. To measure the effect of each shape signature in representing a shape, precision and recall ratio were used. The result showed that the use of centroid distance signature in calculating fourier descriptors is significantly better than other shape signature. The centroid distance fourier descriptors is robust and information preserving. This is due to the centroid distance, which captures both local and global features of the shape. This paper contributes to the use of centroid distance fourier descriptors CeFD in sign language recognition system, which will be employed in robot teleoperation system. The sign language is adopted from American Sign Language ASL fingerspelling. Only static gesture sign is researched. A Volume 48 – No.2, June 2012 9 depth imager is utilized to acquire hand image. The centroid distance fourier descriptors are used as the feature vector of inputted gesture. To perform recognition, the fourier descriptors of inputted hand image are compared with the fourier descriptors of each character stored in the gesture dictionary. To obtain the best recognition rate, different classifiers will be evaluated. The following section will clearly explain our research methodology.

2. METHODOLOGY

An overview about the methodology of this research is illustrated in Figure 1. This research employs depth imager to acquire hand image from the human signers. The use of depth imager has benefit in segmenting hand image. Rather than color-based segmentation, segmentation in depth image is more robust since the lighting variation does not affect the image quality. Contour of the segmented hand image is then used to generate centroid distance signature. Hand contour coordinates are arranged into centroid distance signature with equal arc-length point sampling, results in N sampled signature point. Fourier transform of the centroid distance signature yields fourier descriptors, which represent feature of each hand gesture. Generally, this research is separated into two phases: dictionary build phase and classification phase. In the dictionary build phase, the fourier descriptors of each character are stored into a database to develop gesture dictionary. The gesture dictionary and comparison method are derived from [12]. The classification phase has the similar step, except that the fourier descriptors are compared with the dictionary using distance metric as classification methods. The result of classification phase is the meaning of the acquired gesture sign.

3. FEATURES EXTRACTION

3.1 Hand Segmentation

Microsoft Kinect TM is utilized as depth imager in this research [13]. Human hand is assumed as the closest object in the imager field of view. The closer the object distance to the imager, the lower the voxel depth pixel value is. Threshold operation is applied to the image with a threshold value. The threshold value is acquired by summing the hand depth and the closest object distance. This method is adopted from [14]. Figure 2 shows the original image and the segmented hand image. Figure 2. Depth image top and the segmented hand image bottom

3.2 Centroid Distance Signature

Shape signature is used to represent shape contour of an object. The shape signature itself is a one-dimensional function that is derived from shape contour coordinate. Centroid distance signature is one of several types of shape signature. In this research, the segmented hand image is processed with canny edge detection to extract the hand contour. Afterward, the centroid distance signature of hand images is generated from the hand contour. Three hand shapes with same gesture and their centroid distance signature are shown in Figure 3. The centroid distance signature rt is computed from the coordinates of each contour sequence by applying Equation 1 and Equation 2.       2 1 2 2 c c y t y x t x t r     1 1 , 1 t y L y t x L x c c   2 Depth Image Acquisition Hand Segmentation Hand Contour Extraction Centroid Distance Signature Centroid Distance Fourier Descriptor Classification Classified Gesture Gesture Dictionary Figure 1. Research methodology a b c d Figure 3. Three hand images with same gesture and its centroid distance signature d ; original a- blue , scaled up 50 b- red , rotated 90° c- green