1 3 SignsWorld Q1 Atlas; a benchmark Arabic Sign 4 Language database Q2 5 Samaa M. Shohieb a, * Q3 , Hamdy K. Elminir c , A.M. Riad b 6 a Information Systems Dept., Faculty of Computers and Information Systems, Egypt Q4 7 b Faculty of Computers and Information Systems Faculty, Mansoura University, Egypt 8 c Department of Electrical Engineering, Faculty of Engineering, Kafr El-Sheikh University, Egypt 9 Received 22 April 2013; revised 26 February 2014; accepted 13 March 2014 10 12 KEYWORDS 13 14 Sign language recognition; 15 Manual signs; 16 Non-manual signs; 17 Arabic Sign Language; 18 Database Abstract Research has increased notably in vision-based automatic sign language recognition (ASLR). However, there has been little attention given to building a uniform platform for these purposes. Sign language (SL) includes not only static hand gestures, finger spelling, hand motions (which are called manual signs ‘‘MS”) but also facial expressions, lip reading, and body language (which are called non-manual signs ‘‘NMS”). Building up a database (DB) that includes both MS and NMS is the main first step for any SL recognition task. In addition to this, the Arabic Sign Language (ArSL) has no standard database. For this purpose, this paper presents a DB developed for the ArSL MS and NM signs which we call SignsWorld Atlas. The postures, gestures, and motions included in this DB are collected in lighting and background laboratory conditions. Indi- vidual facial expression recognition and static hand gestures recognition tasks were tested by the authors using the SignsWorld Atlas, achieving a recognition rate of 97% and 95.28%, respectively. Ó 2014 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 19 20 1. Introduction 21 SL is a powerful means of communication among humans. 22 However, Gesturing is rooted deeply in human communication 23 that people often continue gesturing even during a telephone 24 conversation. Vision based hand gesture recognition is the 25 exemplary case in computer vision and has all along attracted 26 researcher’s attention (Ong and Ranganath, 2005). 27 SL recognition has many important applications. It is used 28 in the natural human computer interactions like virtual envi- 29 ronments (Berry, 1998). In addition, SL became powerful 30 enough to fulfill the needs of the deaf people in their day to 31 day life. SL is also a subset of the gestured communication 32 used in deaf-mute society (Khan et al., 2009 and Riad et al., 33 2012). ASLR systems are being developed for daily communi- 34 cation between the deaf and the hearing persons (Wang and 35 Wang, 2006 and Malima et al., 2006). 36 With Toshiba’s media center software (Toshiba Company, 37 2008) users can pause or play videos and music by holding an 38 open palm up to the screen. Make a fist and your hand works 39 as a mouse, by making a cursor move around the screen. * Corresponding author. Q5 E-mail addresses: sm.shohieb@yahoo.com (S.M. Shohieb), hamdy_elminir@eng.kfs.edu.eg (H.K. Elminir), amriad2000@yahoo. com (A.M. Riad). Peer review under responsibility of King Saud University. Production and hosting by Elsevier Journal of King Saud University – Computer and Information Sciences (2014) xxx, xxx–xxx King Saud University Journal of King Saud University – Computer and Information Sciences www.ksu.edu.sa www.sciencedirect.com http://dx.doi.org/10.1016/j.jksuci.2014.03.011 1319-1578 Ó 2014 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JKSUCI 140 No. of Pages 9 31 December 2014 Please cite this article in press as: Shohieb, S.M. et al., SignsWorld Q1 Atlas; a benchmark Arabic Sign Language database Q2 . Journal of King Saud University – Computer and Information Sciences (2014), http://dx.doi.org/10.1016/j.jksuci.2014.03.011